Systems and Methods for Seat Reconfiguration for Autonomous Vehicles

ABSTRACT

Systems and methods for reconfiguring seats of an autonomous vehicle is provided. The method includes obtaining service request data that includes a service selection and request characteristics. The method includes obtaining data describing an initial seat configuration for each of a plurality of seats of an autonomous vehicle assigned to the service request. The initial seat configuration can include a seat position and a seat orientation for each of the plurality of seats. The method includes generating, based on the initial cabin configuration and the service request data, seat adjustment instructions configured to adjust the initial seat configuration of at least one of the seats. The method includes providing the seat adjustment instructions to the autonomous vehicle assigned to the service request.

PRIORITY CLAIM

The present application is based on and claims benefit of U.S.Provisional Application 63/034,426 having a filing date of Jun. 4, 2020,which is incorporated by reference herein.

FIELD

The present disclosure relates generally to autonomous vehicles and,more particularly, seat reconfiguration for autonomous vehicles.

BACKGROUND

An autonomous vehicle can be capable of sensing its environment andnavigating with little to no human input. In particular, an autonomousvehicle can observe its surrounding environment using a variety ofsensors and can attempt to comprehend the environment by performingvarious processing techniques on data collected by the sensors. Givensuch knowledge, an autonomous vehicle can navigate through theenvironment.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or may be learned fromthe description, or may be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to a computingsystem. The computing system can include one or more processors. Thecomputing system can include one or more tangible, non-transitory,computer readable media that collectively store instructions that whenexecuted by the one or more processors cause the computing system toperform operations. The operations can include obtaining service requestdata comprising a service selection from a plurality of services and oneor more request characteristics. The operations can include obtainingdata describing an initial seat configuration for each of a plurality ofseats of an autonomous vehicle assigned to the service request, theinitial seat configuration comprising a seat position and a seatorientation for each of the plurality of seats within a cabin of theautonomous vehicle, the seat orientation comprising an orientation of atleast one of a backrest, a seat base, or a headrest of the seat. Theoperations can include generating, based on the initial cabinconfiguration and the service request data, seat adjustment instructionsconfigured to adjust the initial seat configuration of at least one ofthe plurality of seats of the autonomous vehicle. The operations caninclude providing the seat adjustment instructions to the autonomousvehicle assigned to the service request.

Another aspect of the present disclosure is directed to acomputer-implemented method. The method can include obtaining servicerequest data comprising a service selection from a plurality of servicesand one or more request characteristics. The method can includeobtaining data describing an initial seat configuration for each of aplurality of seats of an autonomous vehicle assigned to the servicerequest, the initial seat configuration comprising a seat position and aseat orientation for each of the plurality of seats within a cabin ofthe autonomous vehicle, the seat orientation comprising an orientationof at least one of a backrest, a seat base, or a headrest of the seat.The method can include generating, based on the initial cabinconfiguration and the service request data, seat adjustment instructionsconfigured to adjust the initial seat configuration of at least one ofthe plurality of seats of the autonomous vehicle. The method can includeproviding the seat adjustment instructions to the autonomous vehicleassigned to the service request.

Another aspect of the present disclosure is directed to one or moretangible, non-transitory, computer readable media that collectivelystore instructions that when executed by one or more processors causethe one or more processors to perform operations. The operations caninclude obtaining service request data comprising a service selectionfrom a plurality of services and one or more request characteristics.The operations can include obtaining data describing an initial seatconfiguration for each of a plurality of seats of an autonomous vehicleassigned to the service request, the initial seat configurationcomprising a seat position and a seat orientation for each of theplurality of seats within a cabin of the autonomous vehicle, the seatorientation comprising an orientation of at least one of a backrest, aseat base, or a headrest of the seat. The operations can includegenerating, based on the initial cabin configuration and the servicerequest data, seat adjustment instructions configured to adjust theinitial seat configuration of at least one of the plurality of seats ofthe autonomous vehicle. The operations can include providing the seatadjustment instructions to the autonomous vehicle assigned to theservice request.

Other example aspects of the present disclosure are directed to othersystems, methods, vehicles, apparatuses, tangible non-transitorycomputer-readable media, and the like for vehicle reconfiguration.

The autonomous vehicle technology described herein can help improve thesafety of passengers of an autonomous vehicle, improve the safety of thesurroundings of the autonomous vehicle, improve the experience of therider and/or operator of the autonomous vehicle, as well as provideother improvements as described herein. Moreover, the autonomous vehicletechnology of the present disclosure can help improve the ability of anautonomous vehicle to effectively provide vehicle services to others andsupport the various members of the community in which the autonomousvehicle is operating, including persons with reduced mobility and/orpersons that are underserved by other transportation options.Additionally, the autonomous vehicle of the present disclosure mayreduce traffic congestion in communities as well as provide alternateforms of transportation that may provide environmental benefits.

These and other features, aspects and advantages of various embodimentswill become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the present disclosure and, together with thedescription, serve to explain the related principles.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art are set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts a block diagram of an example system according to exampleimplementations of the present disclosure;

FIG. 2 depicts a data flow diagram for generating and providing seatadjustment instructions to an autonomous vehicle according to exampleimplementations of the present disclosure;

FIG. 3 depicts an example of service request data according to exampleimplementations of the present disclosure;

FIG. 4 depicts an example of response characteristics data according toexample implementations of the present disclosure;

FIG. 5 depicts an example of initial seat configuration data accordingto example implementations of the present disclosure;

FIG. 6A depicts an example of seat adjustment instructions configured toadjust the seat configurations of an autonomous vehicle to increasecargo capacity of the autonomous vehicle according to exampleimplementations of the present disclosure;

FIG. 6B depicts an example of seat adjustment instructions configured toadjust one or more seat configurations of an autonomous vehicle to atable configuration according to example implementations of the presentdisclosure;

FIG. 6C depicts an example of seat adjustment instructions configured toadjust the seat configurations of an autonomous vehicle to modify thefacing direction of one or more seats according to exampleimplementations of the present disclosure;

FIG. 7 depicts an example configurable seat layout for an autonomousvehicle according to example implementations of the present disclosure;

FIG. 8 depicts configurations for a passenger seat of an autonomousvehicle according to example embodiments of the present disclosure;

FIG. 9 depicts a flowchart of a method for generating and providing seatadjustment instructions to an autonomous vehicle according to aspects ofthe present disclosure;

FIG. 10 depicts a block diagram of an example computing system accordingto example embodiments of the present disclosure; and

FIG. 11 depicts example system components of an example system accordingto example embodiments of the present disclosure.

DETAILED DESCRIPTION

Example aspects of the present application are directed to improvedsystems and methods for dynamic seat reconfiguration for autonomousvehicles. More particularly, a transportation service provider canobtain transportation service request data, and in response, dynamicallyreconfigure a seating arrangement of the autonomous vehicle assigned tothe service request before the autonomous vehicle arrives at the startlocation of the service. The reconfiguration of the seats can be basedon the service request data, which can include request characteristic(s)(e.g., passenger preference data, trip duration, service route, etc.)and/or a service selection (e.g., represented by transportation servicetiers, types of service (e.g., food delivery vs. passenger transport),etc.). By way of example, a computing system can obtain service requestdata that indicates the selection of a premium transportation servicetier. In response to receiving the request data, the computing systemcan obtain data describing an initial seat configuration for each seatof the autonomous vehicle (e.g., seat positioning, a backrestorientation, a seat base orientation, etc.). Based on the initialconfiguration and the service selection, the computing system cangenerate seat adjustment instructions configured to adjust the initialseat configuration of one or more seats in the autonomous vehicle (e.g.,to increase in-cabin leg room for a premium service, etc.). The seatadjustment instructions can be provided to the autonomous vehicleassigned to the service request by the computing system. In suchfashion, the computing system can dynamically adjust the seatingconfiguration of an autonomous vehicle to most optimally providetransportation services by reducing unnecessary time delays/vehicledowntime that may be caused by manual or post-pickup seatreconfiguration.

Aspects of the present application are directed to autonomous vehicleswith dynamic seat reconfiguration to more efficiently provide a numberof specialized services. More particularly, a computing system (e.g., anautonomous vehicle computing system, an operations computing system,etc.) can obtain a service request from a user for a vehicle service.The service request can include and/or be associated with servicerequest data indicating a service provided by an autonomous vehicle. Theautonomous vehicle can be an autonomous vehicle with the capacity toreconfigure its seats based on a number of factors (e.g., the servicerequest data, etc.). The service request data can include a serviceselection. The service selection can indicate a particular service froma plurality of services offered by an autonomous vehicle and anassociated service provider (e.g., food delivery, autonomous humanpassenger transportation, pooled transportation service, deliveryservices, courier services, etc.). As an example, the service requestdata can indicate an autonomous vehicle transportation service at ahighest service tier of a number of service tiers (e.g., a moreluxurious transportation service, etc.). As another example, the servicerequest data may indicate a pooled transportation service (e.g., atransportation service pooled amongst a number of passengers submittingseparate service requests).

It should be noted that the autonomous vehicle providing the serviceselected by the service selection can be an autonomous vehicle capableof a plurality of seat configurations. More particularly, the autonomousvehicle can include one or more seats that can individually orcollectively be reconfigured (e.g., reconfiguration of a seatorientation and/or a seat position). As an example, a seat of theautonomous vehicle can change a location inside the autonomous vehicle(e.g., by sliding longitudinally along a track inside the cabin of theautonomous vehicle, etc.). As another example, a seat of the autonomousvehicle can change an orientation inside the autonomous vehicle (e.g.,fully retracting a headrest in the seat, changing an angle of the seatback of the seat, folding the seat back onto the seat base of the seatto form a table, etc.). In such fashion, the seating arrangement ofseats in the autonomous vehicle can be dynamically reconfigured to moreefficiently provide a number of different services.

The service request data, associated with a user's service request, caninclude one or more request characteristics. The requestcharacteristic(s) can describe aspect(s) of the requestor(s) and/or theselected service. In some implementations, the request characteristic(s)can describe an expected occupancy. As an example, the requestcharacteristic(s) can indicate if a requestor of the service hasindicated additional passengers for an autonomous transportation service(e.g., children, family members, friends, etc.). In someimplementations, the expected occupancy can include occupancypredictions associated with the selected service. As an example, thecomputing system may determine (e.g., using machine-learned model(s),passenger data, etc.) that a service requestor generally travels withmore than one person. As another example, the expected occupancypredictions may indicate a certain number of predicted occupants basedon the service selection (e.g., a service selection for a certain tierof an autonomous transportation service, an pooled transportationservice selection, etc.). The expected occupancy predictions can begenerated using various methods by the computing system (e.g.,machine-learning techniques, deterministic prediction algorithms, etc.).

In some implementations, the expected occupancy predictions can be basedat least in part on previously collected autonomous vehicle sensor dataassociated with the requestor. As an example, previous servicerequest(s) from the requestor can be associated with sensor data thatindicated the presence of two additional passengers in the vehicle(e.g., collected from seat weight sensors, door sensors, etc.). In suchfashion, the computing system can use previously collected sensor datato associate a usual number of additional passengers with a certainrequestor.

In some implementations, the expected occupancy predictions can includea prediction of specific identity(s) of passenger(s) predicted toaccompany the requestor. As an example, a requestor may historicallyrequest a certain tier of an autonomous vehicle transportation servicewhen traveling with a spouse. Based on the service tier selection, thecomputing system can predict that the spouse of the requestor willaccompany the spouse in the autonomous vehicle transportation service.Additionally, in some implementations, the passenger preferences for thespouse of the above example can be included in the expected occupancypredictions. For example, passenger preference data for the spouse mayindicate that the spouse generally prefers a certain seat configurationfor the autonomous vehicle transportation service.

In some implementations, the request characteristic(s) can includepassenger preference data. Passenger preference data can indicate arequestor(s) preferred seat configuration for a vehicle. It should benoted that passenger preference data can refer to passengers of theautonomous vehicle. However, some services do not require a passenger(e.g., a food delivery service, etc.) and as such, passenger orrequestor may be used interchangeably when discussing services of theautonomous vehicle. A “passenger” of the vehicle, as described, canrefer to both the requestor of the service and any additional occupantsof the vehicle. The preferred seat configuration can be based on ananalysis of previous data associated with the requestor (e.g., userscores, user satisfaction determinations, previously selected userconfigurations, etc.). As an example, the passenger preference data mayinclude user score(s) and/or user satisfaction data associated withservices previously requested by the requestor. For example, if therequestor previously requested an autonomous transportation service at acertain tier, and the requestor left a poor review for the service aftercompletion, the seat configuration of the autonomous vehicle whileproviding the service can be disincentivized for selection (by thecomputing system) for subsequent service requests by the requestor.Moreover, a computing system (e.g., its machine-learned models, etc.)can be receive user rating(s), review(s), etc. as a feedback process tolearn which seat configurations were positively received by user(s) andwhich seat configurations were negatively received by user(s). Thecomputing system can re-train/re-learn and adjust a certain seatconfiguration for a selected service in hopes of decreasing the amountof future negative feedback.

In some implementations, the passenger preference data can includeuser-entered data corresponding to the seat configuration of theautonomous vehicle. As an example, the user-entered data may specify acertain seat configuration preference for a number of the services ofthe autonomous vehicle. For example, the user-entered data may indicatethat the user prefers a seat configuration that maximizes cabin spacewhen requesting a highest service tier for an autonomous vehicletransportation service. As another example, the user-entered data mayinclude a request to opt-out of certain seat configurations. Forexample, the user-entered data may include a request that the autonomousvehicle never use a passenger-facing seat configuration (e.g., vehicleseats facing one another, etc.) when requesting a highest service tierfor an autonomous vehicle transportation service.

In some implementations, the passenger preference data can includepreferences for two or more passengers of the autonomous vehicletransportation service. As an example, two requestors may request anautonomous vehicle pooled rideshare service. The passenger preferencedata can include specific passenger preference data for each of the tworequestors. As another example, a single requestor can request anautonomous vehicle transportation service, and the service request canindicate the identity of two additional passengers. The passengerpreference data can include specific passenger preference data for eachof the two additional passengers.

In some implementations, the computing system can aggregate thepassenger preference data for a number of passengers of the autonomousvehicle. More particularly, the passenger preference data can beaggregated such that the aggregated passenger preference data indicatesa seat configuration preference that satisfies the most passengers. Asan example, four passengers can be utilizing an autonomous vehiclepooled rideshare service. Passenger preference data for three of thepassengers can indicate that the passengers prefer a seat configurationwhere passengers do not face each other. Passenger preference data forone passenger can indicate the passenger prefers a seat configurationwhere passengers do face each other. The passenger preference data forall four passengers can be aggregated such that the aggregated passengerpreference data indicates that the passengers prefer a seatconfiguration where passengers do not face each other.

In some implementations, the passenger preference data can indicateaccessibility configurations required by a passenger. As an example,passenger preference data for a passenger who uses a wheelchair canindicate that the passenger requires a seat configuration accessible towheelchair-using occupants (e.g., an open seat configuration tofacilitate wheelchair access, an extendable wheelchair ramp, etc.). Insuch fashion, the passenger preference data can be utilized to quicklyand efficiently provide accessibility services to passengers who wouldpreviously be forced to wait for significant periods of time before aspecially configured vehicle was available for them.

In some implementations, the passenger preference data can indicatepassenger historical data. The historical data can describe previouspassenger preferences and/or behaviors that indicate a preference. Asdescribed previously, the historical data can be utilized by thecomputing system to predict an expected occupancy. As an example, thehistorical data can indicate that the passenger generally travels withtwo additional occupants when requesting an autonomous vehicletransportation service. In some implementations, the historical data canindicate previous behavior that indicates preferred configurations. Asan example, the historical data may indicate that a passenger generallybrings luggage when requesting an autonomous vehicle transportationservice to an airport, and therefore prefers a seat configuration thatmaximizes luggage capacity in the autonomous vehicle when traveling toan airport. The historical data for the passenger can store and utilizeany previous preferences and/or behaviors that relate to the selectionof an autonomous vehicle seat configuration.

In some implementations, the historical data can indicate a passengerexperience level with the autonomous vehicle transportation service.More particularly, the historical data can indicate a number of timesthe requestor and any associated passengers have utilized variousservice(s) of the autonomous vehicle service provider. As an example,the historical passenger data can indicate that a requestor has neverpreviously utilized an autonomous vehicle transportation service. Inresponse, the seat configuration can, in some implementations, beadjusted (e.g., via seat adjustment instructions) to increase thecomfort of the requestor. As another example, the historical data canindicate that a passenger associated with the requestor has neverpreviously utilized an autonomous vehicle transportation service. Inresponse, the seat configuration can, in some implementations, beadjusted (e.g., via seat adjustment instructions) to increase thecomfort of the passenger.

It should be noted that, in some implementations, the passengerpreference data associated with a passenger can be analyzed as it iscollected, utilized to generate and/or modify seat configurationpreferences, and then discarded. More particularly, the passengerpreference data may only contain a passenger's predicted seatconfiguration preferences, without containing any personalizedinformation (e.g., passenger locations, passenger behavior, etc.). Assuch, the passenger preference data can be utilized to optimally predictthe best seat configuration for a passenger without containing anypersonalized data associated with the passenger.

Additionally, or alternatively, in some implementations, passengers andrequestors of services may opt-out of any passenger preference datacollection, and/or can opt-out of the collection of specific passengerpreference data. In some implementations, passengers or requestors ofthe autonomous vehicle may be required to “opt-in” to passengerpreference data collection.

In some implementations, the request characteristic(s) can include anassociated trip service route. The associated trip service route caninclude a start location and an end location for the associated tripservice route. As an example, a requestor can request an autonomousvehicle transportation service from the requestor's location to arestaurant. The start location of the associated trip service route canbe the requestor's location and the end location for the associated tripservice route can be the restaurant.

In some implementations, the associated trip service route can includeroute features for the planned route to be navigated from the startlocation to the end location. In some implementations, the planned routecan be generated by the computing system. Alternatively, in someimplementations, the planned route can be generated by an associatedcomputing system that is communicatively coupled to the computing system(e.g., a route planning computing system, operations computing system,etc.). The route features can be one or more features of the plannedroute. The route features can include a highest speed, a type ofsteering required for the route, a route environment, a route duration,or any other type of information relevant to the route traveled by theautonomous vehicle from the start location to the end location.

As an example, the route features can indicate that the routeenvironment is generally considered a scenic route (e.g., based onaggregated passenger preference data, weather data, etc.) that is easilyviewed from certain windows of the autonomous vehicle (e.g., viewing theocean from the left side of the autonomous vehicle, etc.). As anotherexample, the route features may indicate that the route requires anumber of sharp cornering maneuvers generally unsuitable for certainseat configurations. As yet another example, the route features mayindicate that the planned route requires high-speed highway travel thatis generally considered unsuitable for certain seat configurations.

In some implementations, the route features can be aggregated over timealongside passenger data to better predict the effect of route features(e.g., using machine-learning techniques, etc.). As an example, thecomputing system can generally associate passenger data indicatingpassenger sickness with certain seat configurations and high speedcornering. As another example, the computing system can generallyassociate passenger data indicating discomfort with certain seatconfigurations and highway travel. In such fashion, the computing systemcan predict an optimal seat configuration based on the route features ofthe planned route.

In response to receiving the service request data, the computing systemcan obtain data describing an initial seat configuration for each of aplurality of seats of an autonomous vehicle assigned to the servicerequest. In some implementations, the assigned autonomous vehicle can beassigned to provide the service by the computing system. Alternatively,in some implementations, an associated computing system can assign theautonomous vehicle to provide the service (e.g., an operations computingsystem, etc.). In some implementations, the data describing the initialseat configuration can be obtained directly from the autonomous vehicle(e.g., via a network, etc.). Alternatively, in some implementations, thedata describing the initial seat configuration can be obtained from acomputing system associated with the autonomous vehicle (e.g., athird-party computing system, a routing computing system, etc.).Alternatively, in some implementations, if the computing system is anautonomous vehicle computing system (e.g., a computing system locatedonboard the autonomous vehicle, etc.) the data describing the initialseat configuration can be obtained from the sensors and/or a computingdevice of the autonomous vehicle.

The initial seat configuration for each seat of the autonomous vehiclecan include a seat position for the seat within the cabin of theautonomous vehicle. The seat position for the seat within the cabin canbe a longitudinal and/or lateral position inside the cabin. As anexample, if viewing the exterior doors of the vehicle, the seat can belocated longitudinally along the side of the vehicle. More particularly,the seat position can correspond to a position on a track (e.g., a seattrack located in the floor of the autonomous vehicle, etc.) that spansthe interior of the autonomous vehicle. The seat position can bereconfigured by moving the seat along the seat track inside theautonomous vehicle. In such fashion, the seat position of the initialseat configuration can describe a longitudinal position on the tracklocated in the floor of the autonomous vehicle.

It should be noted that, in some implementations, not all seats of theautonomous vehicle are necessarily connected to the track inside thefloor of the autonomous vehicle. Instead, the seat position can describea folded position of a seat in the autonomous vehicle that is notconnected to a track inside the autonomous vehicle. More particularly, afirst seat of the autonomous vehicle can be configured to fold forwardto attach to the back of a second seat of the autonomous vehicle.Further, the first seat can be configured to unfold from the back of theseat to a default seat position. In such fashion, one seat can fold andunfold from the back of another seat inside the autonomous vehicle.

The initial seat configuration for each seat of the autonomous vehiclecan include a seat orientation for each of the seats in the autonomousvehicle. The seat orientation can include a backrest, a seat base,and/or a headrest orientation. The seat backrest can be the back-supportcomponent of the seat. The seat backrest orientation can be configuredto move about an angle where the seat backrest attaches to a seat baseof the seat. As an example, the seat backrest can fold to be parallel tothe seat base of the seat. As another example, the seat backrest can bepositioned to be perpendicular to the seat base of the seat. The seatbase, similar to the seat back, can move about an angle of the seat toadjust a sitting angle. Additionally, the seat base can movelongitudinally about an axis.

In some implementations, the seat base can be moved concurrently withthe seat backrest to switch a facing direction of the seat. As anexample, the seat can be facing a first direction (e.g., a passengersitting in the seat would be looking in the first direction). The seatbackrest can move in conjunction with the seat base such that the seatcan subsequently face a second direction opposite that of the firstdirection (e.g., facing away from one another).

The seat orientation can describe an orientation of the headrest of aseat. The orientation of the headrest can correspond to a lateralposition of the headrest of the seat. More particularly, the headrestorientation can refer to a degree of retraction of the headrest into thebackrest of the seat. As an example, the headrest can be oriented tofully retract inside the backrest of the seat (e.g., to facilitateconfiguration of the seat into a table configuration, etc.). As anotherexample, the headrest orientation can be configured to fully extendoutside the backrest of the seat.

In some implementations, the initial seat configuration for each seat ofthe autonomous vehicle can include a seat direction for each of theseats in the autonomous vehicle. The seat direction for a seat candescribe a direction the seat is facing relative to other aspects of theautonomous vehicle. More particularly, the seat direction can be definedas the direction that a passenger of the seat would face. As an example,a front facing seat direction can describe a seat direction in which apassenger sitting in the seat would face longitudinally towards thefront section of the autonomous vehicle. As another example, a rearfacing seat direction can describe a seat direction in which a passengersitting in the seat would face longitudinally towards the rear sectionof vehicle. As yet another example, a side facing seat direction candescribe a seat direction in which a passenger sitting in the seat wouldface laterally towards a side section of the vehicle (e.g., tofacilitate viewing of natural scenery through vehicle windows, etc.). Insuch fashion, the seat direction of the initial seat configuration candescribe any seat direction of a seat relative to aspects of theautonomous vehicle.

In response to receiving the service request data, the computing systemcan generate seat adjustment instructions. The seat adjustmentinstructions can be based on the initial cabin configuration and theservice request data. The seat adjustment instructions can be configuredto adjust an initial seat configuration of at least one seat of theplurality of seats inside the cabin of the autonomous vehicle. In someimplementations, the seat adjustment instructions can be configured toadjust the initial seat configuration to maximize an amount of cabinspace (e.g., based on the service request, etc.). The instructions cando so by adjusting the initial seat configuration so that the seats arepositioned on the longitudinal edges of the cabin of the autonomousvehicle. For example, the instructions can be configured to adjust aseat back orientation, a headrest orientation, a seat bottomorientation, and/or a seat position of a seat of the autonomous vehicle.

In some implementations, the seat adjustment instructions can beconfigured to adjust the seat configurations such that the seats do notface one another (e.g., based on passenger preferences indicatingpassengers prefer to avoid eye contact, etc.). As an example, the seatscan be initially configured to face one another. The seat adjustmentinstructions can be configured to adjust the seat configuration so thatthe seat backrest and seat base move in such a way that the seats faceaway from one another. Additionally, the seat adjustment instructionscan move the position of the seats so that the backs of opposing seatsare adjacent to each other in the middle of the autonomous vehiclecabin. In such fashion, the seat adjustment instructions can change aninitial passenger-facing seat configuration to a more “bench-like” seatconfiguration where the backs of the seats are positioned are againsteach other.

Alternatively, in some implementations, the seat adjustment instructionscan be configured to adjust the seat configurations such that the seatsdo face one another (e.g., based on passenger preference data indicatingthat two passengers know each other, etc.). As an example, the fare data(e.g., the fare data received in the service request) can indicate thattwo passengers (e.g., picked-up or dropped-off at the same location) aresplitting a fare for an autonomous vehicle transportation service. Inresponse, the seat adjustment instructions can be configured to adjustthe seat configuration so that the seat backrest and the seat base movein such a way that the seats face each other (e.g., to facilitate eyecontact, conversation, etc.). In such fashion, the seat adjustmentinstructions can change an initial same-facing “bench-like” seatconfiguration to a “passenger-facing” seat configuration to facilitatecommunication and comfort between friends and family.

In some implementations, the seat adjustment instructions can beconfigured to adjust the seat configurations such that one or more seatsin a row are staggered such that the one seat is positioned slightlyfurther in a longitudinal direction than the one or more other seats. Asan example, three seats can initially be configured to be positioned ina row, where each seat is positioned in the exact same longitudinalposition (e.g., each seat is on an individual track where thelongitudinal position of each seat on each track is the same). The seatadjustment instructions can be configured to adjust the longitudinalposition of the middle of the three seats so that the middle seat ispositioned further longitudinally in the direction the seats are facingthan the two side seats (e.g., based on expected occupancy dataindicating a certain number of passengers, etc.). As another example,two seats can initially be configured to be positioned in a row, whereeach seat is positioned in the exact same longitudinal position. Theseat adjustment instructions can be configured to adjust thelongitudinal position of one of the two seats so that the seat ispositioned further longitudinally in the direction the seats are facingthan the other seat. In such fashion, one or more seats can bepositioned slightly forward in a seat-facing direction than the otherseat(s) in the row, therefore reducing the chance that passengerssitting in the seats in the row will rub shoulders.

In some implementations, the seat adjustment instructions can beconfigured to adjust the seat configurations such that one or more ofthe seats are folded into a table configuration (e.g., based onpassenger preference data indicating that a passenger is likely to workand/or to eat during the service, etc.). As an example, the seats can beinitially configured to face one another. The seat adjustmentinstructions can be configured to adjust the seat configuration so thatthe seat backrest of one seat folds to a position parallel to the seatbase (e.g. both the seat base and the seat backrest parallel to thefloor of the autonomous vehicle, etc.). Additionally, the seatadjustment instructions can move the position of the folded seat so thatthe seat is directly in front of a passenger and a relatively shortdistance from the passenger. In such fashion, the seat adjustmentinstructions can change an initial passenger-facing seat configuration“table” configuration such that the passenger can use the backrest ofthe seat as a table for eating or working during the autonomoustransportation service. Such a seat configuration can be associated witha “business” or “work” type of service selection such that a passengermay utilize the table as a working surface.

In some implementations, the seat adjustment instructions be based atleast in part on a destination location. The seat adjustmentinstructions can be configured to adjust the seat configurations to acargo capacity configuration such that seats of the autonomous vehicleare folded and/or moved to maximize the cargo capacity of the autonomousvehicle (e.g., based on a food delivery service selection, an airportdestination, etc.). For example, the destination of the vehicle can bean airport location (e.g., based on the request characteristics, etc.).Based on the airport destination, the seat adjustment instructions canmove the seat position and the seat orientation of one or more seats inthe cabin to maximize a cargo capacity of the autonomous vehicle (e.g.,folding one or more seats, etc.). It should be noted that in someimplementations, the cargo capacity configuration can have an occupiedmode and a non-occupied mode. The occupied mode can configure the seatsto maximize a cargo space in the vehicle while still allowing passengersto occupy the vehicle (e.g., maintaining some seats in an unfoldedposition, etc.), while the non-occupied mode can configure the seats tomaximize the cargo space in the vehicle without regard to the occupancyof the vehicle (e.g., folding all seats in the vehicle, etc.).

As described previously, the seat adjustment instructions can be basedat least in part on the service request data. As an example, the seatadjustment instructions can be based on the service selection. Forexample, the seat configuration can be adjusted to maximize cargo spacebased on a food delivery service selection. For another example, theseat configuration can be adjusted to maximize the passenger capacity ofthe vehicle based on an autonomous vehicle rideshare pooling serviceselection. Further, the seat adjustment instructions can be based on therequest characteristic(s). As an example, the seat configuration can beadjusted to maximize passenger capacity based on an expected occupancyprediction. As another example, the seat configuration can be adjustedto maximize the comfort of one passenger based on passenger preferencedata. As yet another example, the seat configuration can be adjusted toa seat configuration that provides accessibility features based onaccessibility data (e.g., the capability to fit a wheelchair, etc.).

In some implementations, generating the seat adjustment instructions caninclude detecting, by the computing system using one or more sensors inthe cabin of the autonomous vehicle, one or more objects inside thecabin of the autonomous vehicle. More particularly, the computing systemcan utilize sensor(s) in the cabin of the autonomous vehicle (e.g.,camera(s), weight sensor(s), etc.) to detect objects inside theautonomous vehicle (e.g., forgotten briefcases, toys, smartphones,etc.). Based on the detected objects, the computing system can generateand/or modify the seat adjustment instructions. As an example, thecomputing system can determine, based on the initial seat configurationand the service request data, to fold three seats in a row to a foldedposition. The computing system can detect a briefcase left by apassenger in the middle seat. In response, the computing system cangenerate seat adjustment instructions that fold the outer two seats inthe row into a folded position while keeping the middle seat (e.g., theseat with the briefcase) in an upright position to avoid damaging thebriefcase and/or the seat.

Alternatively, in some implementations, the example described abovecould instead lead the autonomous vehicle to take a separate action. Asan example, the autonomous vehicle may forego generating any seatadjustment instructions. Instead, the autonomous vehicle may provide anotification to the passenger associated with the item (e.g., includingit should be removed, has been left, etc.). As another example, theautonomous vehicle may return to a maintenance location so that theobject can be removed. It should be noted that the autonomous vehiclecan take any sort of action in response to detecting the presence of anobject in the interior of the cabin of the autonomous vehicle.

In some implementations, the computing system can utilize sensor data todetermine the efficiency of various seat configuration(s) (e.g., usingmachine-learning techniques, etc.). More particularly, the computingsystem can analyze various performance characteristics (e.g., passengeringress and/or egress, luggage loading and/or unloading, etc.) based onthe sensor data to determine seat configuration efficiency forfacilitating passenger utilization of the autonomous vehicle. As anexample, sensor data (e.g., weight sensor, image data, motion detectordata, etc.) can be analyzed to determine that passenger ingress takesmore time than average when utilizing a certain seat configuration.Based on the amount of time, the computing system can determine and/orassign a seat configuration efficiency value to the seat configuration.The computing system can evaluate the seat efficiency value whengenerating seat adjustment instructions.

Additionally, in some implementations, the computing system canassociate seat configuration efficiency with request characteristicsdata. More particularly, a seat efficiency value can be correlated tocertain request characteristics (e.g., a number of passengers, an amountof luggage, a trip duration, a number of stops, etc.) In such fashion, afirst seat configuration could be associated with a plurality of seatefficiency values, each seat efficiency value correlated to differentcircumstances indicated by request characteristic data. As an example,the computing system can assign a first seat efficiency value to a firstseat configuration when three passengers are traveling in the vehicle.The computing system can then assign a second seat efficiency value tothe first seat configuration when one passenger is traveling in thevehicle. When evaluating seat configuration efficiency to generate seatadjustment instructions, the computing system can utilize the seatefficiency value that corresponds to the current circumstances indicatedby the request characteristic(s). In such fashion, the computing systemcan utilize such information as a feedback learning process to determinethe most efficient seat configuration for the circumstances indicated bythe request characteristics (e.g., a number of passengers, a certaindestination, a trip duration, etc.). For example, machine-learned modelsutilized to determine/recommend seat configurations can be re-trained onsuch data to refine future determinations/recommendations.

In response to receiving the service request data, the computing systemcan provide the seat adjustment instructions to the autonomous vehicle.In some implementations, the computing system can be separate from theautonomous vehicle and can provide the seat adjustment instructions tothe vehicle (e.g., via networks, associated first and/or third-partycomputing systems, etc.). Alternatively, in some implementations, thecomputing system can be included in or otherwise be an autonomousvehicle computing system of the autonomous vehicle (e.g., physicallylocated onboard the autonomous vehicle, etc.), and can thereforedirectly adjust the seats of the autonomous vehicle based on the seatadjustment instructions.

It should be noted that, in some implementations, the autonomous vehiclecan be configured to adjust the seats of the autonomous vehicle based onthe seat adjustment instructions before passenger(s) enter the vehicle.More particularly, the autonomous vehicle can begin and complete seatreconfiguration (e.g., based on the adjustment instructions) beforeallowing occupants to enter the vehicle. In such fashion, the autonomousvehicle can prevent any accidental injury to passengers accessing theautonomous vehicle.

In addition, or alternatively, the operations computing system canprovide for fleet-wide reconfigurations by providing seat adjustmentinstructions to a plurality of autonomous vehicles. For instance, thecomputing system (e.g., an operations computing system of an autonomousvehicle service provider, etc.) can determine that a plurality ofvehicles can be reconfigured based on one or more external factors(e.g., demand curve matching, load balancing, high capacityincentivization in peak demand times/locations, emergency evacuationsituation (e.g., due to weather, etc.), etc.). It should be noted thatthese one or more external factors can, in some implementations, bedetermined based at least in part on analysis of a plurality ofcollected service requests and/or data from external sources (e.g.,machine-learned analysis of recent news stories or other real-time datasources, etc.).

As an example, the computing system (e.g., the operations computingsystem of an autonomous vehicle service, etc.) can determine, based on anumber of collected service requests, that a demand curve spike isoccurring. Based on the detection of the demand curve spike, a number ofautonomous vehicles (e.g., a regional/localized fleet of autonomousvehicles, etc.) can be supplied with seat adjustment instructions thatmaximize a passenger occupancy of the vehicle. As another example, thecomputing system can determine, based at least in part on a number ofcollected service requests and a machine-learned analysis of recentinformation sources, that an emergency situation is occurring in ageographic area (e.g., a natural disaster, car accident, etc.). Based onthe detection of the emergency situation, autonomous vehicles in thegeographic area can be supplied with seat adjustment instructions tofacilitate transport of affected people (e.g., a maximized passengercapacity, a configuration to facilitate transportation of injuredpersons, etc.). It should be noted that in the detection of an emergencysituation, the computing system can also temporarily reduce or eliminatethe costs associated with all services provided by the reconfiguredautonomous vehicles. In such fashion, the computing system can quicklyand efficiently facilitate safe travel for a maximum number of users ofthe autonomous vehicle service that are affected by an emergencysituation.

As another example, the computing system can determine that a number ofautonomous vehicles located in a certain geographic area (e.g., ahigh-density urban area, a low-density rural area, etc.) should eachreconfigure seating configurations using the same seat adjustmentinstructions. For example, the computing system can determine from anumber of service requests that the vast majority of service requests ina high-density urban area prefer a seat configuration that maximizes anumber of passengers of the autonomous vehicle (e.g., to lower anassociated ride cost, etc.). In response, the computing system canprovide seat adjustment instructions to every or most autonomousvehicles in the high-density urban area to reconfigure the autonomousvehicles to a seating configuration that maximizes a number ofpassengers of the autonomous vehicle. In such fashion, the computingsystem can determine an optimal default configuration for an entirefleet of autonomous vehicles and/or a subset of a fleet of autonomousvehicles, and can reconfigure a desired number of vehicles concurrently(e.g., based on market demand, collated service request data, etc.).

Various means can be configured to perform the methods and processesdescribed herein. For example, a computing system can include servicerequest receiving unit(s), seat configuration obtaining unit(s), seatadjustment instruction generation unit(s), seat adjustment instructionproviding unit(s), and/or other means for performing the operations andfunctions described herein. In some implementations, one or more of theunits may be implemented separately. In some implementations, one ormore units may be a part of or included in one or more other units.These means can include processor(s), microprocessor(s), graphicsprocessing unit(s), logic circuit(s), dedicated circuit(s),application-specific integrated circuit(s), programmable array logic,field-programmable gate array(s), controller(s), microcontroller(s),and/or other suitable hardware. The means can also, or alternately,include software control means implemented with a processor or logiccircuitry, for example. The means can include or otherwise be able toaccess memory such as, for example, one or more non-transitorycomputer-readable storage media, such as random-access memory, read-onlymemory, electrically erasable programmable read-only memory, erasableprogrammable read-only memory, flash/other memory device(s), dataregistrar(s), database(s), and/or other suitable hardware.

The means can be programmed to perform one or more algorithm(s) forcarrying out the operations and functions described herein. Forinstance, the means can be configured to obtain data (e.g., servicerequest data) from service requestor that indicates a service selectionand one or more request characteristics for the service request. Aservice request obtaining unit is an example of means obtaining suchdata from an autonomous vehicle at an operations computing system asdescribed herein.

The means can be configured to obtain an initial seat configuration foreach seat in an autonomous vehicle. For example, the means can beconfigured to obtain initial seat configuration data that describes aseat position and a seat orientation for each seat in an autonomousvehicle assigned to a service request. A seat configuration obtainingunit is one example of a means for obtaining an initial seatconfiguration for one or more seats of an autonomous vehicle asdescribed herein.

The means can be configured to generate seat adjustment instructions.For example, the means can be configured to generate, based on theinitial seat configuration and the service request data, generate seatadjustment instructions configured to adjust the seat position and/orthe seat orientation of at least one seat. A seat adjustment instructiongeneration unit is one example of a means for generating seat adjustmentinstructions configured to reconfigure one or more seats of anautonomous vehicle as described herein.

The means can be configured to provide seat adjustment instructions. Forexample, the means can be configured to provide the generated seatadjustment instructions to an autonomous vehicle (e.g., via a network,one or associated computing systems, etc.). A seat adjustmentinstruction providing unit is one example of a means for allocatingexcess computational resources as described herein.

The present disclosure provides a number of technical effects andbenefits. As one example technical effect and benefit, the systems andmethods of the present disclosure enable a single vehicle to provide avariety of autonomous vehicle services. More particularly, the presentdisclosure can reconfigure the seating arrangements of autonomousvehicles in real-time to facilitate a variety of different services. Byallowing a single vehicle to provide a variety of different autonomousvehicle services, the present disclosure can drastically reduce thenumber of vehicles utilized by autonomous vehicle service providers,which can, in turn, reduce traffic congestion and environmental damage.Further, by dynamically reconfiguring the seating of an autonomousvehicle, an autonomous vehicle service provider can utilize any vehicleto perform any service, allowing the autonomous vehicle service providerto quickly provide any service to a service requestor instead of waitingfor a specialized vehicle configured to provide the service.

While the present subject matter has been described in detail withrespect to specific example embodiments and methods thereof, it will beappreciated that those skilled in the art, upon attaining anunderstanding of the foregoing can readily produce alterations to,variations of, and equivalents to such embodiments. Accordingly, thescope of the present disclosure is by way of example rather than by wayof limitation, and the subject disclosure does not preclude inclusion ofsuch modifications, variations and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.With reference to the figures, example embodiments of the presentdisclosure will be discussed in further detail.

FIG. 1 depicts a block diagram of an example system 100 for controllingthe navigation of a vehicle according to example embodiments of thepresent disclosure. As illustrated, FIG. 1 shows an example system 100that can include an autonomous vehicle 102, an operations computingsystem 104, one or more remote computing devices 106, a communicationnetwork 108, a vehicle computing system 112, one or more sensors 114,sensor data 116, a positioning system 118, an autonomy computing system120, map data 122, a perception system 124, a prediction system 126, amotion planning system 128, state data 130, prediction data 132, motionplan data 134, a communication system 136, a vehicle control system 138,a human-machine interface 140, a seat control system 142, and a doorcontrol system 144.

The operations computing system 104 can be associated with a serviceprovider (e.g., service entity) that can provide one or more vehicleservices to a plurality of users via a fleet of vehicles (e.g., serviceentity vehicles, third-party vehicles, etc.) that includes, for example,the autonomous vehicle 102. The vehicle services can includetransportation services (e.g., rideshare services), courier services,delivery services, and/or other types of services.

The operations computing system 104 can include multiple components forperforming various operations and functions. For example, the operationscomputing system 104 can include and/or otherwise be associated with theone or more computing devices that are remote from the autonomousvehicle 102. The one or more computing devices of the operationscomputing system 104 can include one or more processors and one or morememory devices. The one or more memory devices of the operationscomputing system 104 can store instructions that when executed by theone or more processors cause the one or more processors to performoperations and functions associated with the operation of one or morevehicles (e.g., a fleet of vehicles), with the provision of vehicleservices, and/or other operations as discussed herein.

For example, the operations computing system 104 can be configured tomonitor and communicate with the autonomous vehicle 102 and/or its usersto coordinate a vehicle service provided by the autonomous vehicle 102.To do so, the operations computing system 104 can manage a database thatstores data including vehicle status data associated with the status ofvehicles including autonomous vehicle 102. The vehicle status data caninclude a state of a vehicle, a location of a vehicle (e.g., a latitudeand longitude of a vehicle), the availability of a vehicle (e.g.,whether a vehicle is available to pick-up or drop-off passengers and/orcargo, etc.), and/or the state of objects internal and/or external to avehicle (e.g., the physical dimensions and/or appearance of objectsinternal/external to the vehicle).

The operations computing system 104 can communicate with the one or moreremote computing devices 106 and/or the autonomous vehicle 102 via oneor more communications networks including the communications network108. The communications network 108 can exchange (send or receive)signals (e.g., electronic signals) or data (e.g., data from a computingdevice) and include any combination of various wired (e.g., twisted paircable) and/or wireless communication mechanisms (e.g., cellular,wireless, satellite, microwave, and radio frequency) and/or any desirednetwork topology (or topologies). For example, the communicationsnetwork 108 can include a local area network (e.g. intranet), wide areanetwork (e.g. Internet), wireless LAN network (e.g., via Wi-Fi),cellular network, a SATCOM network, VHF network, a HF network, a WiMAXbased network, and/or any other suitable communications network (orcombination thereof) for transmitting data to and/or from the autonomousvehicle 102.

Each of the one or more remote computing devices 106 can include one ormore processors and one or more memory devices. The one or more memorydevices can be used to store instructions that when executed by the oneor more processors of the one or more remote computing devices 106 causethe one or more processors to perform operations and/or functionsincluding operations and/or functions associated with the autonomousvehicle 102 including exchanging (e.g., sending and/or receiving) dataor signals with the autonomous vehicle 102, monitoring the state of theautonomous vehicle 102, and/or controlling the autonomous vehicle 102.The one or more remote computing devices 106 can communicate (e.g.,exchange data and/or signals) with one or more devices including theoperations computing system 104 and the autonomous vehicle 102 via thecommunications network 108.

The one or more remote computing devices 106 can include one or morecomputing devices (e.g., a desktop computing device, a laptop computingdevice, a smart phone, and/or a tablet computing device) that canreceive input or instructions from a user or exchange signals or datawith an item or other computing device or computing system (e.g., theoperations computing system 104). Further, the one or more remotecomputing devices 106 can be used to determine and/or modify one or morestates of the autonomous vehicle 102 including a location (e.g.,latitude and longitude), a velocity, acceleration, a trajectory, and/ora path of the autonomous vehicle 102 based in part on signals or dataexchanged with the autonomous vehicle 102. In some implementations, theoperations computing system 104 can include the one or more remotecomputing devices 106.

The autonomous vehicle 102 can be a ground-based vehicle (e.g., anautomobile, bike, scooter, other light electric vehicle, etc.), anaircraft, and/or another type of vehicle. The autonomous vehicle 102 canperform various actions including driving, navigating, and/or operating,with minimal and/or no interaction from a human driver. The autonomousvehicle 102 can be configured to operate in one or more modes including,for example, a fully autonomous operational mode, a semi-autonomousoperational mode, a park mode, and/or a sleep mode. A fully autonomous(e.g., self-driving) operational mode can be one in which the autonomousvehicle 102 can provide driving and navigational operation with minimaland/or no interaction from a human driver present in the vehicle. Asemi-autonomous operational mode can be one in which the autonomousvehicle 102 can operate with some interaction from a human driverpresent in the vehicle. Park and/or sleep modes can be used betweenoperational modes while the autonomous vehicle 102 performs variousactions including waiting to provide a subsequent vehicle service,and/or recharging between operational modes.

An indication, record, and/or other data indicative of the state of thevehicle, the state of one or more passengers of the vehicle, and/or thestate of an environment including one or more objects (e.g., thephysical dimensions and/or appearance of the one or more objects) can bestored locally in one or more memory devices of the autonomous vehicle102. Additionally, the autonomous vehicle 102 can provide dataindicative of the state of the vehicle, the state of one or morepassengers of the vehicle, and/or the state of an environment to theoperations computing system 104, which can store an indication, record,and/or other data indicative of the state of the one or more objectswithin a predefined distance of the autonomous vehicle 102 in one ormore memory devices associated with the operations computing system 104(e.g., remote from the vehicle). Furthermore, the autonomous vehicle 102can provide data indicative of the state of the one or more objects(e.g., physical dimensions and/or appearance of the one or more objects)within a predefined distance of the autonomous vehicle 102 to theoperations computing system 104, which can store an indication, record,and/or other data indicative of the state of the one or more objectswithin a predefined distance of the autonomous vehicle 102 in one ormore memory devices associated with the operations computing system 104(e.g., remote from the vehicle).

The autonomous vehicle 102 can include and/or be associated with thevehicle computing system 112. The vehicle computing system 112 caninclude one or more computing devices located onboard the autonomousvehicle 102. For example, the one or more computing devices of thevehicle computing system 112 can be located on and/or within theautonomous vehicle 102. The one or more computing devices of the vehiclecomputing system 112 can include various components for performingvarious operations and functions. For instance, the one or morecomputing devices of the vehicle computing system 112 can include one ormore processors and one or more tangible, non-transitory, computerreadable media (e.g., memory devices). The one or more tangible,non-transitory, computer readable media can store instructions that whenexecuted by the one or more processors cause the autonomous vehicle 102(e.g., its computing system, one or more processors, and other devicesin the autonomous vehicle 102) to perform operations and functions,including those described herein.

As depicted in FIG. 1 , the vehicle computing system 112 can include oneor more sensors 114, the positioning system 118, the autonomy computingsystem 120, the communication system 136, the vehicle control system(s)138, and the human-machine interface 140. One or more of these systemscan be configured to communicate with one another via a communicationchannel. The communication channel can include one or more data buses(e.g., controller area network (CAN)), on-board diagnostics connector(e.g., OBD-II), and/or a combination of wired and/or wirelesscommunication links. The onboard systems can exchange (e.g., send and/orreceive) data, messages, and/or signals amongst one another via thecommunication channel.

The sensor(s) 114 can include a plurality of external sensors (e.g.,LiDAR sensors, outward facing cameras, etc.) and/or internal sensors(e.g., tactile sensors (e.g., touch sensors within seats of a vehicleinterior, on the handle of a vehicle door, etc.), internal facingmicrophones, internal facing cameras, etc.). As discussed herein, theinternal sensor(s) and/or external sensor(s) can be utilized by thevehicle computing system 112 to gather internal sensor data associatedwith a vehicle 102 such as, for example, occupancy data identifying thestate (e.g., the position and/or orientation) of one or more passengersriding within the vehicle 102.

More particularly, the vehicle computing system 112 can include and/orbe associated with a plurality of external sensors (e.g., LiDAR sensors,outward facing cameras, etc.) and/or interior sensors (e.g., internalfacing cameras/heat sensors, internal facing microphones, tactilesensors (e.g., touch sensors within seats of a vehicle interior, on thehandle of a vehicle door, etc.), etc.). The sensor(s) 114 can be locatedon various parts of the autonomous vehicle 102 including the vehicleinterior, a front side, rear side, left side, right side, top, or bottomof the vehicle body, etc. For instance, the sensor(s) 114 can be placedthroughout the vehicle to obtain sensor data indicative of the presenceof objects and/or humans currently and/or predicted to be within and/orproximate to the vehicle's interior. The sensor data, for example, canbe obtained by the interior sensors such as one or more camerasconfigured to obtain image data, one or more microphones configured toobtain auditory data, one or more tactile sensors configured to obtaintactile data (e.g., to detect a touch to a seat to determine whether anobject and/or passenger is placed on or sitting in a passenger seat,etc.), heat sensor(s), weight sensor(s), etc. In addition, oralternatively, the sensor data can be obtained by the external sensorssuch as one or more external sensors configured to detect a passenger orobject in the process of entering and/or exiting the vehicle's interior.For instance, the external sensors can include infrared sensors thatwrap around the vehicle's body (e.g., a side of the vehicle thatincludes an entry and/or exit to the vehicle, etc.), camera(s), LiDARsensors, microphones, tactile sensors (e.g., to detect a touch to a door(e.g., a handle of the door) of the vehicle, etc.), etc. In addition,other sensors can be utilized to generate and/or obtain sensor data suchas, for example, ultrasonic sensors, RADAR sensor (e.g., placed alongthe side of the vehicle, etc.) and/or any other sensor capable ofgenerating and/or obtaining data indicative of an object and/orpassenger's proximity to the vehicle 102.

The vehicle computing system 112 can be configured to process the sensordata 116 to detect objects and/or passengers (e.g., an elbow, hand,foot, etc.) relative to an area (e.g., zone) within the vehicle interiorand/or an entry or exit of the vehicle's interior. By way of example,the vehicle computing system 112 can utilize one or more sensorprocessing models (image processing and/or or any other sensorprocessing model(s)) configured to detect the objects and/or passengers.For instance, the sensor processing models can include one or moremachine-learned models learned to analyze the sensor data 116 and/or oneor more portions of the sensor data 116 and output an indication of thelocation, heading, and/or other information for any passenger(s) and/orobject(s) proximate to or within the vehicle 102.

In some implementations, the sensor processing models can includemultiple machine-learned models configured to output the same and/orsimilar information based on one or more different portions of thesensor data 116 (e.g., detection information based on image data,detection information based on tactile data, etc.). The redundancy frommultiple sensor suites and/or processing models can confirm and/orincrease the vehicle computing system's confidence in the detection ofthe one or more objects and/or passengers. In some implementations, thesensor processing models can include the same machine-learned modelsused by one or more perception 124 and/or predictions systems 126 of theautonomy computing system 120 (as described in further detail below). Inaddition, or alternatively, the sensor processing models can includedifferent machine-learned models that use algorithms/models similar tothe models used by the one or more perception 124 and/or predictionsystems 126.

In some implementations, the vehicle 102 can include one or more sensorycues (e.g., visual cues such as paint, contouring, lighting, etc.) onone or more interior (e.g., passenger seats, etc.) and/or exterior(e.g., passenger doors, etc.) components of the vehicle 102. The sensorycues can be used to enhance the detection accuracy of the one or moresensor processing models. For example, the one or more sensory cues cangive a frame of reference for one or more portions of the vehicle 102.By way of example, as discussed in greater detail herein, the vehicle102 can include a plurality of zones identifying different portions ofthe vehicle 102. In some implementations, the vehicle 102 can includeone or more sensory cues that define each of the plurality of portions.By way of example, the sensory cues can include paint, electricalsignals, reflective surfaces, edging/contouring, etc. that identify aparticular portion (e.g., a door, a front portion of the vehicleinterior, etc.) of the vehicle 102. In this manner, the one or moresensor processing models can compare the location of one or more objectsand/or passengers relative to the one or more sensory cues to determinewhether an object and/or passenger is located proximate to one or morezones of the vehicle.

The sensor(s) 114 can be configured to generate and/or store dataincluding the sensor data 116. The sensor data 116 can include theinternal sensor data, external sensor discussed above, and well anautonomy sensor data associated with one or more objects that areproximate to the autonomous vehicle 102 (e.g., within range or a fieldof view of one or more of the one or more sensors 114 (e.g., externalsensor(s)). For instance, the sensor(s) 114 can include a LightDetection and Ranging (LIDAR) system, a Radio Detection and Ranging(RADAR) system, one or more cameras (e.g., visible spectrum camerasand/or infrared cameras), motion sensors, and/or other types of imagingcapture devices and/or sensors. The autonomy sensor data can includeimage data, radar data, LIDAR data, and/or other data acquired by thesensor(s) 114. The one or more objects can include, for example,pedestrians, vehicles, bicycles, and/or other objects. The autonomysensor data can be indicative of locations associated with the one ormore objects within the surrounding environment of the autonomousvehicle 102 at one or more times. For example, the autonomy sensor datacan be indicative of one or more LIDAR point clouds associated with theone or more objects within the surrounding environment. The sensor(s)114 can provide autonomy sensor data to the autonomy computing system120.

In addition to the sensor data 116, the autonomy computing system 120can retrieve or otherwise obtain data including the map data 122. Themap data 122 can provide detailed information about the surroundingenvironment of the autonomous vehicle 102. For example, the map data 122can provide information regarding: the identity and location ofdifferent roadways, road segments, buildings, or other items or objects(e.g., lampposts, crosswalks and/or curb), the location and directionsof traffic lanes (e.g., the location and direction of a parking lane, aturning lane, a bicycle lane, or other lanes within a particular roadwayor other travel way and/or one or more boundary markings associatedtherewith), traffic control data (e.g., the location and instructions ofsignage, traffic lights, or other traffic control devices), and/or anyother map data that provides information that assists the vehiclecomputing system 112 in processing, analyzing, and perceiving itssurrounding environment and its relationship thereto.

The vehicle computing system 112 can include a positioning system 118.The positioning system 118 can determine a current position of theautonomous vehicle 102. The positioning system 118 can be any device orcircuitry for analyzing the position of the autonomous vehicle 102. Forexample, the positioning system 118 can determine position by using oneor more of inertial sensors, a satellite positioning system, based onIP/MAC address, by using triangulation and/or proximity to networkaccess points or other network components (e.g., cellular towers and/orWi-Fi access points) and/or other suitable techniques. The position ofthe autonomous vehicle 102 can be used by various systems of the vehiclecomputing system 112 and/or provided to one or more remote computingdevices (e.g., the operations computing system 104 and/or the remotecomputing device 106). For example, the map data 122 can provide theautonomous vehicle 102 relative positions of the surrounding environmentof the autonomous vehicle 102. The autonomous vehicle 102 can identifyits position within the surrounding environment (e.g., across six axes)based at least in part on the data described herein. For example, theautonomous vehicle 102 can process the autonomy sensor data (e.g., LIDARdata, camera data) to match it to a map of the surrounding environmentto get an understanding of the vehicle's position within thatenvironment (e.g., transpose the autonomous vehicle's 102 positionwithin its surrounding environment).

The autonomy computing system 120 can include a perception system 124, aprediction system 126, a motion planning system 128, and/or othersystems that cooperate to perceive the surrounding environment of theautonomous vehicle 102 and determine a motion plan for controlling themotion of the autonomous vehicle 102 accordingly. In some examples, manyof the functions performed by the perception system 124, predictionsystem 126, and motion planning system 128 can be performed, in whole orin part, by a single system and/or multiple systems that share one ormore computing resources. For instance, one or more of the perceptionsystem 124, prediction system 126, and motion planning system 128 can becombined into one system configured to perform the functions of each ofthe systems. In addition, or alternatively, the one or more of theperception system 124, prediction system 126, and motion planning system128 can be configured to share and/or have access to one or more commoncomputing resources (e.g., a shared memory, communication interfaces,processors, etc.).

As an example, the autonomy computing system 120 can receive the sensordata 116 from the one or more sensors 114, attempt to determine thestate of the surrounding environment and/or the vehicle's interior byperforming various processing techniques on the sensor data 116 (and/orother data). The autonomy computing system 120 can generate anappropriate motion plan through the surrounding environment based onstate of the surrounding environment and the vehicle's interior. In someexamples, the autonomy computing system 120 can use the sensor data 116as input to a one or more machine-learned models that can detect objectswithin the sensor data 116, forecast future motion of those objects, andselect an appropriate motion plan for the autonomous vehicle 102. Themachine-learned model(s) can be included within one system and/or shareone or more computing resources.

As another example, the perception system 124 can identify one or moreobjects that are proximate to and/or within the autonomous vehicle 102based on sensor data 116 received from the sensor(s) 114. In particular,in some implementations, the perception system 124 can determine, foreach object, state data 130 that describes the current state of suchobject. As examples, the state data 130 for each object can describe anestimate of the object's: current location (e.g., relative to one ormore interior vehicle components, the surrounding environment of thevehicle, etc.); current speed; current heading (which may also bereferred to together as velocity); current acceleration; currentorientation (e.g., with respect to the direction of travel of thevehicle, etc.); size/footprint (e.g., as represented by a bounding shapesuch as a bounding polygon or polyhedron); class of characterization(e.g., vehicle class versus pedestrian class versus bicycle class versusother class); yaw rate; and/or other state information. In someimplementations, the perception system 124 can determine state data 130for each object over a number of iterations. In particular, theperception system 124 can update the state data 130 for each object ateach iteration. Thus, the perception system 124 can detect and trackobjects (e.g., vehicles, bicycles, pedestrians, etc.) that are proximateand/or within the autonomous vehicle 102 over time, and thereby producea presentation of the world around and within the vehicle 102 along withits state (e.g., a presentation of the objects of interest within ascene/vehicle interior at the current time along with the states of theobjects).

The prediction system 126 can receive the state data 130 from theperception system 124 and predict one or more future locations and/ormoving paths for each object based on such state data 130. For example,the prediction system 126 can generate prediction data 132 associatedwith each of the respective one or more objects proximate and/or withinthe vehicle 102. The prediction data 132 can be indicative of one ormore predicted future locations of each respective object. Theprediction data 132 can be indicative of a predicted path (e.g.,predicted trajectory) of at least one object within the interior and/orthe surrounding environment of the autonomous vehicle 102. For example,the predicted path (e.g., trajectory) can indicate a path along whichthe respective object is predicted to travel over time (and/or thevelocity at which the object is predicted to travel along the predictedpath). The prediction system 126 can provide the prediction data 132associated with the one or more objects to the motion planning system128.

The motion planning system 128 can determine a motion plan and generatemotion plan data 134 for the autonomous vehicle 102 based at least inpart on the prediction data 132 (and/or other data). The motion plandata 134 can include vehicle actions with respect to the objectsproximate to the autonomous vehicle 102 as well as the predictedmovements. For instance, the motion planning system 128 can implement anoptimization algorithm that considers cost data associated with avehicle action as well as other objective functions (e.g., costfunctions based on speed limits, traffic lights, and/or other aspects ofthe environment), if any, to determine optimized variables that make upthe motion plan data 134. By way of example, the motion planning system128 can determine that the autonomous vehicle 102 can perform a certainaction (e.g., pass an object) without increasing the potential risk tothe autonomous vehicle 102 and/or violating any traffic laws (e.g.,speed limits, lane boundaries, signage). The motion plan data 134 caninclude a planned trajectory, velocity, acceleration, and/or otheractions of the autonomous vehicle 102.

As one example, in some implementations, the motion planning system 128can determine a cost function for each of one or more candidate motionplans for the autonomous vehicle 102 based at least in part on thecurrent locations and/or predicted future locations and/or moving pathsof the objects. For example, the cost function can describe a cost(e.g., over time) of adhering to a particular candidate motion plan. Forexample, the cost described by a cost function can increase when theautonomous vehicle 102 approaches impact with another object and/ordeviates from a preferred pathway (e.g., a predetermined travel route).

Thus, given information about the current locations and/or predictedfuture locations and/or moving paths of objects, the motion planningsystem 128 can determine a cost of adhering to a particular candidatepathway. The motion planning system 128 can select or determine a motionplan for the autonomous vehicle 102 based at least in part on the costfunction(s). For example, the motion plan that minimizes the costfunction can be selected or otherwise determined. The motion planningsystem 128 then can provide the selected motion plan to a vehiclecontrol system 138 that controls one or more vehicle controls (e.g.,actuators or other devices that control gas flow, steering, braking,etc.) to execute the selected motion plan.

The motion planning system 128 can provide the motion plan data 134 withdata indicative of the vehicle actions, a planned trajectory, and/orother operating parameters to the vehicle control systems 138 toimplement the motion plan data 134 for the autonomous vehicle 102.

The vehicle computing system 112 can include a communications system 136configured to allow the vehicle computing system 112 (and it's one ormore computing devices) to communicate with other computing devices. Thevehicle computing system 112 can use the communications system 136 tocommunicate with the operations computing system 104 and/or one or moreother remote computing devices (e.g., the one or more remote computingdevices 106) over one or more networks (e.g., via one or more wirelesssignal connections, etc.). In some implementations, the communicationssystem 136 can allow communication among one or more of the systemson-board the autonomous vehicle 102. The communications system 136 canalso be configured to enable the autonomous vehicle to communicate withand/or provide and/or receive data and/or signals from a remotecomputing device 106 associated with a user and/or an item (e.g., anitem to be picked-up for a courier service). The communications system136 can utilize various communication technologies including, forexample, radio frequency signaling and/or Bluetooth low energy protocol.The communications system 136 can include any suitable components forinterfacing with one or more networks, including, for example, one ormore: transmitters, receivers, ports, controllers, antennas, and/orother suitable components that can help facilitate communication. Insome implementations, the communications system 136 can include aplurality of components (e.g., antennas, transmitters, and/or receivers)that allow it to implement and utilize multiple-input, multiple-output(MIMO) technology and communication techniques.

The vehicle computing system 112 can include one or more human-machineinterfaces 140. For example, the vehicle computing system 112 caninclude one or more display devices located on the vehicle computingsystem 112. A display device (e.g., screen of a tablet, laptop, and/orsmartphone) can be viewable by a user of the autonomous vehicle 102 thatis located in the front of the autonomous vehicle 102 (e.g., driver'sseat, front passenger seat). Additionally, or alternatively, a displaydevice can be viewable by a user of the autonomous vehicle 102 that islocated in the rear of the autonomous vehicle 102 (e.g., a passengerseat in the back of the vehicle).

In some implementations, the vehicle computing system 112 can include aseat control system 142 and/or a door control system 144. The seatcontrol system 142 can be configured to control the operation of one ormore configurable seats positioned within the interior of the autonomousvehicle 102. For instance, the seat control system 142 can include oneor more actuators (e.g., electric motors) configured to control movementof the one or more configurable seats. As will be discussed herein, theseat control system 142 can configure the interior of the autonomousvehicle 102 to accommodate a plurality of different seatingconfigurations.

The door control system 144 can be configured to control the operationof one or more door assemblies to permit access to the interior of thevehicle 102. For instance, the door control system 144 can include oneor more actuators (e.g., electric motors) configured to control movementof the door assembly(s). More specifically, the one or more actuatorscan move the one or more door assemblies between an open position and aclosed position to permit selective access to the interior of theautonomous vehicle 102. In addition, or alternatively, the door controlsystem 144 can be configured to selectively lock and/or unlock the doorassembly(s). In such a case, the door assembly(s) can permit themovement (e.g., from a closed position to an open position and/or viceversa) of the door assembly(s) when unlocked and prevent movement of thedoor assembly(s) when unlocked.

FIG. 2 depicts a data flow diagram 200 for generating and providing seatadjustment instructions to an autonomous vehicle according to exampleimplementations of the present disclosure. More particularly, servicerequestor device(s) 202 can send service request data 208 to computingsystem 204. Service requestor device(s) 202 can be any device (e.g.,smartphone, tablet, smart device, computing device, etc.) associatedwith and/or corresponding to a user of an autonomous vehicle service(e.g., a rideshare service, delivery service, transportation service,courier service, etc.). The service request data 208 can be sent via anynetwork and/or transmission medium (e.g., WiFi, cellular network(s),etc.). The service request data 208 can include a service selection andone or more request characteristics. The service selection can indicatea particular service selected by the requestor(s) associated with theservice requestor device(s) 202. The service selection of the servicerequest data 208 can be a service selected from a plurality of servicesoffered by an autonomous vehicle service associated with computingsystem 204 (e.g., food delivery, autonomous human passengertransportation, pooled transportation service, delivery services,courier services, etc.). As an example, the service request data 208 canindicate an autonomous vehicle transportation service at a highestservice tier of a number of service tiers (e.g., a more luxurioustransportation service, etc.). As another example, the service requestdata 208 may indicate a pooled transportation service (e.g., atransportation service pooled amongst a number of passengers submittingseparate service request data 208).

The service request data 208, associated with the service requestordevice 202, can include one or more request characteristics. The requestcharacteristic(s) can describe aspect(s) of the user(s) of the servicerequestor device(s) and/or the selected service. In someimplementations, the request characteristic(s) can describe an expectedoccupancy. As an example, the request characteristic(s) can indicate ifa user of the service requestor device 202 has indicated additionalpassengers for an autonomous transportation service (e.g., children,family members, friends, etc.). In some implementations, the expectedoccupancy can include occupancy predictions associated with the serviceselection. As an example, a computing system (e.g., computing system204) may determine (e.g., using machine-learned model(s), passengerdata, etc.) that the user of the service requestor device 202 generallytravels with more than one person. As another example, the expectedoccupancy predictions may indicate a certain number of predictedoccupants based on the service selection (e.g., a service selection fora certain tier of an autonomous transportation service, a pooledtransportation service selection, etc.). The expected occupancypredictions can be generated using various methods by computing system204 (e.g., machine-learning techniques, deterministic predictionalgorithms, etc.).

The computing system 204 can receive the service request data 208. Thecomputing system 204 can be any number of computing systems and/ordevices associated with the autonomous vehicle service. As an example,the computing system 204 can be or otherwise include the operationscomputing system(s) 104 of FIG. 1 . As another example, the computingsystem 204 can be or otherwise include a third-party intermediary system(e.g., of a third party autonomous vehicle provider, etc.) configured todirectly interface with computing systems of the autonomous vehicleservice (e.g., through one or more application programming interfaces,etc.).

The computing system 204 can generate an initial seat configurationrequest 210. The initial seat configuration request 210 can beconfigured to request data describing an initial seat configuration foran autonomous vehicle 206 (e.g., the vehicle 102 of FIG. 1 , etc.). Inresponse, the autonomous vehicle 206 can send data describing theinitial seat configuration 212 to the computing system 204.Alternatively, in some implementations, the data describing the initialseat configuration 212 can be obtained from a computing systemassociated with the autonomous vehicle 206 (e.g., a third-partycomputing system, a routing computing system, etc.). Alternatively, insome implementations, if the computing system is an autonomous vehiclecomputing system (e.g., a computing system located onboard theautonomous vehicle 206, etc.) the data describing the initial seatconfiguration 212 can be obtained from the sensors and/or a computingdevice of the autonomous vehicle 206.

The initial seat configuration data 212 can describe, for each seat ofthe autonomous vehicle, a seat position for the seat within the cabin ofthe autonomous vehicle 206. The seat position for the seat within thecabin can be a longitudinal and/or lateral position inside the cabin ofthe autonomous vehicle 206. As an example, if viewing the exterior doorsof the autonomous vehicle 206, the seat can be located longitudinallyalong the side of the autonomous vehicle 206. More particularly, theseat position can correspond to a position on a track (e.g., a seattrack located in the floor of the autonomous vehicle 206, etc.) thatspans the interior of the autonomous vehicle 206. The seat position canbe reconfigured by moving the seat along the seat track inside theautonomous vehicle 206. In such fashion, the seat position of theinitial seat configuration data 212 can describe a longitudinal positionon the track located in the floor of the autonomous vehicle 206.

It should be noted that, in some implementations, not all seats of theautonomous vehicle 206 are necessarily connected to the track inside thefloor of the autonomous vehicle 206. Instead, the seat position candescribe a folded position of a seat in the autonomous vehicle 206 thatis not connected to a track inside the autonomous vehicle 206. Moreparticularly, a first seat of the autonomous vehicle 206 can beconfigured to fold forward to attach to the back of a second seat of theautonomous vehicle 206. Further, the first seat can be configured tounfold from the back of the seat to a default seat position. In suchfashion, one seat can fold and unfold from the back of another seatinside the autonomous vehicle 206.

The initial seat configuration data 212 for each seat of the autonomousvehicle 206 can include seat orientation data for each of the seats inthe autonomous vehicle 206. The seat orientation data can include abackrest, a seat base, and/or a headrest orientation. The seat backrestcan be the back-support component of the seat. The seat backrestorientation can be configured to move about an angle where the seatbackrest attaches to a seat base of the seat. As an example, the seatbackrest can fold to be parallel to the seat base of the seat. Asanother example, the seat backrest can be positioned to be perpendicularto the seat base of the seat. The seat base, similar to the seat back,can move about an angle of the seat to adjust a sitting angle.Additionally, the seat base can move longitudinally about an axis. Theseat orientation can define the direction in which an accompanyingpassenger would be facing.

In some implementations, the seat base can be moved concurrently withthe seat backrest to switch a facing direction of the seat. As anexample, the seat can be facing a first direction (e.g., a passengersitting in the seat would be looking in the first direction). The seatbackrest can move in conjunction with the seat base such that the seatcan subsequently face a second direction opposite that of the firstdirection (e.g., facing away from one another).

The seat orientation data can describe an orientation of the headrest ofa seat. The orientation of the headrest can correspond to a lateralposition of the headrest of the seat. More particularly, the headrestorientation can refer to a degree of retraction of the headrest into thebackrest of the seat. As an example, the headrest can be oriented tofully retract inside the backrest of the seat (e.g., to facilitateconfiguration of the seat into a table configuration, etc.). As anotherexample, the headrest orientation can be configured to fully extendoutside the backrest of the seat

After receiving the data describing the initial seat configuration 212,the computing system 204 can generate seat adjustment instructions 214for the autonomous vehicle 206. The seat adjustment instructions 214 canbe based on the data describing the initial cabin configuration 212 andthe service request data 208. The seat adjustment instructions 214 canbe configured to adjust an initial seat configuration of at least oneseat of the plurality of seats inside the cabin of the autonomousvehicle 206. In some implementations, the seat adjustment instructions214 can be configured to adjust the initial seat configuration tomaximize an amount of cabin space (e.g., based on the service request,etc.) in the autonomous vehicle 206. The seat adjustment instructions214 can do so by adjusting the initial seat configuration so that theseats are positioned on the longitudinal edges of the cabin of theautonomous vehicle 206. For example, the seat adjustment instructions214 can be configured to adjust a seat back orientation, a headrestorientation, a seat bottom orientation, and/or a seat position of a seatof the autonomous vehicle 206.

In some implementations, generating the seat adjustment instructions 214can include detecting, by the computing system 204 using one or moresensors in the cabin of the autonomous vehicle 206, one or more objectsinside the cabin of the autonomous vehicle 206. More particularly, thecomputing system 204 can utilize sensor(s) in the cabin of theautonomous vehicle 206 (e.g., camera(s), weight sensor(s), etc.) todetect objects inside the autonomous vehicle 206 (e.g., forgottenbriefcases, toys, smartphones, etc.). Based on the detected objects, thecomputing system 204 can generate and/or modify the seat adjustmentinstructions 214. As an example, the computing system 204 can determine,based on the data describing the initial seat configuration 212 and theservice request data 208, to fold three seats in a row to a foldedposition. The computing system 204 can detect a briefcase left by apassenger in the middle seat of the autonomous vehicle 206. In response,the computing system 204 can generate seat adjustment instructions 214that fold the outer two seats in the row into a folded position whilekeeping the middle seat (e.g., the seat with the briefcase) in anupright position to avoid damaging the briefcase and/or the seat.

Alternatively, in some implementations, the example described abovecould instead lead the autonomous vehicle 206 to take a separate action.As an example, if the computing system 204 is located onboard theautonomous vehicle 206, the autonomous vehicle 206 may forego generatingany seat adjustment instructions 214. Instead, the autonomous vehicle206 may provide a notification to the passenger (e.g., the userassociated with the service requestor device(s) 202) that are associatedwith the item (e.g., including it should be removed, has been left,etc.). As another example, the autonomous vehicle 206 may return to amaintenance location so that the object can be removed. It should benoted that the autonomous vehicle 206 can take any sort of action inresponse to detecting the presence of an object in the interior of thecabin of the autonomous vehicle 206.

The computing system 204 can provide the seat adjustment instructions214 to the autonomous vehicle 206. In some implementations, thecomputing system 204 can be separate from the autonomous vehicle 206 andcan provide the seat adjustment instructions 214 to the autonomousvehicle 206 by transmitting the instructions (e.g., via networks,associated first and/or third-party computing systems, etc.).Alternatively, in some implementations, the computing system 204 can beincluded in or otherwise be an autonomous vehicle computing system ofthe autonomous vehicle 206 (e.g., the vehicle computing system 112 ofFIG. 1 , etc.), and can therefore directly adjust the seats of theautonomous vehicle 206 based on the seat adjustment instructions 214.

It should be noted that, in some implementations, the autonomous vehicle206 can be configured to adjust the seats of the autonomous vehicle 206based on the seat adjustment instructions 214 before passenger(s) enterthe autonomous vehicle 206. More particularly, the autonomous vehicle206 can begin and complete seat reconfiguration (e.g., based on theadjustment instructions 214) before allowing occupants to enter thevehicle. In such fashion, the autonomous vehicle 206 can prevent anyaccidental injury to passengers accessing the autonomous vehicle 206.

The computing system 204 can receive seat adjustment status data 216from the autonomous vehicle 206. The seat adjustment status data 216 candescribe any aspect of the utilization of the seat adjustmentinstructions 214 by the autonomous vehicle 206. In some implementations,the seat adjustment status data 216 can indicate that the autonomousvehicle 206 was unable to carry out at least some of the seat adjustmentinstructions 214. As an example, the seat adjustment status data 216 canindicate that the autonomous vehicle 206 did not reconfigure a seatbecause of an object left in the seat by a passenger. As anotherexample, the seat adjustment status data 216 can indicate that theautonomous vehicle did not reconfigure a seat due to a broken seatconfiguration mechanism (e.g., a failure in a pneumatic adjustmentsystem of the seat, a foreign object lodged in the track of the seatpositioning system, etc.). In some implementations, the seat adjustmentstatus data 216 can include sensor data from any sensors onboard theautonomous vehicle 206.

In some implementations, the computing system 204 can utilize seatadjustment status data 216 to determine the efficiency of various seatconfiguration(s) (e.g., using machine-learning techniques, etc.). Moreparticularly, the computing system 204 can analyze various performancecharacteristics (e.g., passenger ingress and/or egress, luggage loadingand/or unloading, etc.) based on the seat adjustment status data 216 todetermine seat configuration efficiency for facilitating passengerutilization of the autonomous vehicle 206. As an example, seatadjustment status data 216 (e.g., weight sensor data, image data, motiondetector data, etc.) can be analyzed to determine that passenger ingresstakes more time than average when utilizing a certain seat configurationthat corresponds to the seat adjustment instructions 214. Based on theamount of time, the computing system 204 can determine and/or assign aseat configuration efficiency value to the seat configuration describedby the seat adjustment instructions 214. The computing system 204 canevaluate the seat efficiency value when generating additional seatadjustment instructions 214 in the future.

The computing system 204 can communicate seat adjustment status data 218to the service requestor device(s) 202 (e.g., via one or more networks,one or more third-party computing systems, etc.). The seat adjustmentstatus data 218 can utilized by the service requestor device(s) 202(e.g., via an application of the autonomous service provider associatedwith computing system 204, etc.) to provide the service requestor(s)with the status of the autonomous vehicle's interior seat configuration.As an example, the seat adjustment status data 216 can indicate that theseat adjustment instructions 214 were at least partially unable to beperformed. In response, the seat adjustment status data 218 can beconfigured to indicate to the service requestor that a preferred and/orrequested seat configuration is unavailable. As another example, theseat adjustment status data 216 can indicate that the seat adjustmentinstructions 214 were successfully performed in full. In response, theseat adjustment status data 218 can be configured to indicate to theservice requestor that a preferred and/or requested seat configurationis available. In some implementations, if the seat adjustment statusdata 218 are configured to indicate to the service requestor that apreferred and/or requested seat configuration is unavailable, theapplication associated with the autonomous vehicle service provider canadditionally prompt the service requestor whether the service requestorwould prefer to utilize the autonomous vehicle 206 in the current seatconfiguration or if the service requestor would prefer to wait for adifferent autonomous vehicle 206.

FIG. 3 depicts an example of service request data 300 according toexample implementations of the present disclosure. Service request data300 can be or otherwise be included in any form of data structure (e.g.,a table, array, etc.). Service request data 300 can include requestor(s)identifier 302. In some implementations, requestor(s) identifier 302 canbe identification data associated with one or more users of anautonomous vehicle service provider that requested an autonomous vehicleservice (e.g., a transportation service, rideshare service, etc.). As anexample, requestor(s) identifier 302 can be a unique user ID for a userof an autonomous vehicle transportation service. As another example,requestor(s) identifier 302 can be a plurality of unique user ID's for aplurality of users that have requested an autonomous vehicle rideshareservice (e.g., a pooled rideshare service, etc.). It should be notedthat the requestor(s) identifier 302 can be any type or format ofidentification data that enables identification of the requestor(s)associated with the service request data 300.

The service request data 300 can include service selection 304. Theservice selection 304 can indicate a particular service from a pluralityof services offered by an autonomous vehicle and an associated serviceprovider (e.g., food delivery, autonomous human passengertransportation, pooled transportation service, delivery services,courier services, etc.). As an example, the service selection 304 of theservice request data 300 can indicate an autonomous vehicletransportation service at a highest service tier of a number of servicetiers (e.g., a more luxurious transportation service, etc.). As anotherexample, the service selection 304 of the service request data 300 mayindicate a pooled transportation service (e.g., a transportation servicepooled amongst a number of passengers submitting separate servicerequests).

It should be noted that the autonomous vehicle providing the serviceselected by the service selection 304 can be an autonomous vehiclecapable of a plurality of seat configurations. More particularly, theautonomous vehicle can include one or more seats that can individuallyor collectively be reconfigured (e.g., reconfiguration of a seatorientation and/or a seat position). As an example, a seat of theautonomous vehicle can change a location inside the autonomous vehicle(e.g., by sliding longitudinally along a track inside the cabin of theautonomous vehicle, etc.). As another example, a seat of the autonomousvehicle can change an orientation inside the autonomous vehicle (e.g.,fully retracting a headrest in the seat, changing an angle of the seatback of the seat, folding the seat back onto the seat base of the seatto form a table, etc.). In such fashion, the seating arrangement ofseats in the autonomous vehicle can be dynamically reconfigured to moreefficiently provide a number of different services.

The service request data 300, associated with a service requestor(s)service request, can include one or more request characteristics 306.The request characteristic(s) 306 can describe aspect(s) of therequestor(s) (e.g., identified by requestor(s) identifier 302) and/orthe selected service (e.g., indicated by service selection 304, etc.).In some implementations, the request characteristic(s) 306 can describean expected occupancy. Additionally, or alternatively, in someimplementations, the request characteristic(s) 306 can include passengerpreferences, user-specified requests, historical data, startlocation(s), end location(s), route features, and/or trip duration. Thespecific implementation of request characteristic(s) 306 will bediscussed in greater detail with regards to FIG. 4 .

FIG. 4 depicts an example of request characteristic(s) data 400according to example implementations of the present disclosure. Requestcharacteristic(s) data 400 can be or otherwise be included in any formof data structure (e.g., a table, array, etc.). Requestcharacteristic(s) data 400 can include requestor(s) identifier 402,which can be analogous to or the same as requestor(s) identifier 302 asdescribed in FIG. 300 .

The request characteristic(s) data 400 can include expected occupancydata 404. As an example, the expected occupancy data 404 can indicate ifa requestor of the service has indicated additional passengers for anautonomous transportation service (e.g., children, family members,friends, etc.). In some implementations, the expected occupancy data 404can include occupancy predictions associated with the selected service.As an example, the computing system may determine (e.g., usingmachine-learned model(s), passenger data, etc.) that a service requestorgenerally travels with more than one person. As another example, theexpected occupancy data 404 predictions may indicate a certain number ofpredicted occupants based on the service selection (e.g., a serviceselection for a certain tier of an autonomous transportation service, apooled transportation service selection, etc.). The expected occupancydata 404 predictions can be generated using various methods by thecomputing system (e.g., machine-learning techniques, deterministicprediction algorithms, etc.).

The request characteristic(s) data 400 can include passenger preferencesdata 406. Passenger preferences data 406 can indicate a requestor(s)preferred seat configuration for a vehicle. It should be noted thatpassenger preference data 406 can refer to passengers of the autonomousvehicle. However, some services do not require a passenger (e.g., a fooddelivery service, etc.) and as such, passenger or requestor may be usedinterchangeably when discussing services of the autonomous vehicle. A“passenger” of the vehicle, as described, can refer to both therequestor of the service (e.g., associated with requestor(s) identifier402) and any additional occupants of the vehicle. The preferred seatconfiguration can be based on an analysis of previous data associatedwith the requestor (e.g., user scores, user satisfaction determinations,previously selected user configurations, etc.). As an example, thepassenger preference data 406 may include user score(s) and/or usersatisfaction data associated with services previously requested by therequestor. For example, if the requestor previously requested anautonomous transportation service at a certain tier, and the requestorleft a poor review for the service after completion, the seatconfiguration of the autonomous vehicle while providing the service canbe disincentivized for selection (by the computing system) forsubsequent service requests by the requestor. Moreover, a computingsystem (e.g., its machine-learned models, etc.) can be receive userrating(s), review(s), etc. as a feedback process to learn which seatconfigurations were positively received by user(s) and which seatconfigurations were negatively received by user(s). The computing systemcan re-train/re-learn and adjust a certain seat configuration for aselected service in hopes of decreasing the amount of future negativefeedback.

In some implementations, the passenger preference data can includepreferences for two or more passengers of the autonomous vehicletransportation service. As an example, two requestors may request anautonomous vehicle pooled rideshare service. The passenger preferencedata can include specific passenger preference data for each of the tworequestors. As another example, a single requestor can request anautonomous vehicle transportation service, and the service request canindicate the identity of two additional passengers. The passengerpreference data can include specific passenger preference data for eachof the two additional passengers.

In some implementations, the computing system can aggregate thepassenger preference data 406 for a number of passengers of theautonomous vehicle. More particularly, the passenger preference data 406can be aggregated such that the aggregated passenger preference data 406indicates a seat configuration preference that satisfies the mostpassengers. As an example, four passengers can be utilizing anautonomous vehicle pooled rideshare service. Passenger preference data406 for three of the passengers can indicate that the passengers prefera seat configuration where passengers do not face each other. Passengerpreference data 406 for one passenger can indicate the passenger prefersa seat configuration where passengers do face each other. The passengerpreference data 406 for all four passengers can be aggregated such thatthe aggregated passenger preference data 406 indicates that thepassengers prefer a seat configuration where passengers do not face eachother.

In some implementations, the passenger preference data 406 can indicateaccessibility configurations required by a passenger. As an example,passenger preference data 406 for a passenger who uses a wheelchair canindicate that the passenger requires a seat configuration accessible towheelchair-using occupants (e.g., an open seat configuration tofacilitate wheelchair access, an extendable wheelchair ramp, etc.). Insuch fashion, the passenger preference data 406 can be utilized toquickly and efficiently provide accessibility services to passengers whowould previously be forced to wait for significant periods of timebefore a specially configured vehicle was available for them.

The request characteristic(s) data 400 can include user-specifiedrequest data 408. In some implementations, the user-specified requestdata 408 can include user-entered data corresponding to the seatconfiguration of the autonomous vehicle. As an example, theuser-specified request data 408 may specify a certain seat configurationpreference for a number of the services of the autonomous vehicle. Forexample, the user-specified request data 408 may indicate that the userprefers a seat configuration that maximizes cabin space when requestinga highest service tier for an autonomous vehicle transportation service.As another example, the user-specified request data 408 may include arequest to opt-out of certain seat configurations. For example, theuser-specified request data 408 may include a request that theautonomous vehicle avoid using a passenger-facing seat configuration(e.g., vehicle seats facing one another, etc.) when requesting a highestservice tier for an autonomous vehicle transportation service.

The request characteristic(s) data 400 can include historical data 410.The historical data 410 can describe previous passenger preferencesand/or behaviors that indicate a preference. As described previously,the historical data 410 can be utilized by the computing system topredict an expected occupancy. As an example, the historical data 410can indicate that the passenger generally travels with two additionaloccupants when requesting an autonomous vehicle transportation service.In some implementations, the historical data 410 can indicate previousbehavior that indicates preferred configurations. As an example, thehistorical data 410 may indicate that a passenger generally bringsluggage when requesting an autonomous vehicle transportation service toan airport, and therefore prefers a seat configuration that maximizesluggage capacity in the autonomous vehicle when traveling to an airport.The historical data 410 for the passenger can store and utilize anyprevious preferences and/or behaviors that relate to the selection of anautonomous vehicle seat configuration.

In some implementations, the historical data 410 can indicate apassenger experience level with the autonomous vehicle transportationservice. More particularly, the historical data 410 can indicate anumber of times the requestor and any associated passengers haveutilized various service(s) of the autonomous vehicle service provider.As an example, the historical data 410 can indicate that a requestor(e.g., indicated by requestor(s) identifier 402) has never previouslyutilized an autonomous vehicle transportation service. In response, theseat configuration can, in some implementations, be adjusted (e.g., viaseat adjustment instructions) to increase the comfort of the requestor.As another example, the historical data 410 can indicate that apassenger associated with the requestor has never previously utilized anautonomous vehicle transportation service. In response, the seatconfiguration can, in some implementations, be adjusted (e.g., via seatadjustment instructions) to increase the comfort of the passenger.

It should be noted that, in some implementations, the passengerpreference data 400 and/or historical data 410 associated with apassenger can be analyzed as it is collected, utilized to generateand/or modify seat configuration preferences, and then discarded. Moreparticularly, the passenger preference data 400 may only contain apassenger's predicted seat configuration preferences, without containingany personalized information (e.g., passenger locations, passengerbehavior, etc.). As such, the passenger preference data 400 can beutilized to optimally predict the best seat configuration for apassenger without containing any personalized data associated with thepassenger.

Additionally, or alternatively, in some implementations, passengers andrequestors of services may opt-out of any passenger preference data 400collection, and/or can opt-out of the collection of specific passengerpreference data 400. In some implementations, passengers or requestorsof the autonomous vehicle may be required to “opt-in” to passengerpreference data collection.

The request characteristic(s) data 400 can include start location data412 and end location data 414. The start location data 412 and endlocation data 414 and respectively describe a start and end location ofa route. Both the start location data 412 and end location data 414 canindicate location(s) using any conventional coordinate system (e.g., GPScoordinates, etc.).

The request characteristic(s) data 400 can include route feature data416. Route feature data 416 can describe the route the planned route tobe navigated from the start location 412 to the end location 414. Insome implementations, the planned route described by the route featuredata 416 can be generated by the computing system. Alternatively, insome implementations, the planned route can be generated by anassociated computing system that is communicatively coupled to thecomputing system (e.g., a route planning computing system, operationscomputing system, etc.). The route feature data 416 can describe one ormore features of the planned route. The route feature data 416 caninclude a highest speed, a type of steering required for the route, aroute environment, a route duration, or any other type of informationrelevant to the route traveled by the autonomous vehicle from the startlocation to the end location.

As an example, the route feature data 416 can indicate that the routeenvironment is generally considered a scenic route (e.g., based onaggregated passenger preference data, weather data, etc.) that is easilyviewed from certain windows of the autonomous vehicle (e.g., viewing theocean from the left side of the autonomous vehicle, etc.). As anotherexample, the route feature data 416 may indicate that the route requiresa number of sharp cornering maneuvers generally unsuitable for certainseat configurations. As yet another example, the route feature data 416may indicate that the planned route requires high-speed highway travelthat is generally considered unsuitable for certain seat configurations.

In some implementations, the route feature data 416 can be aggregatedover time alongside passenger data to better predict the effect of routefeatures (e.g., using machine-learning techniques, etc.). As an example,the computing system can generally associate passenger preference data406 indicating passenger sickness with route feature data 416 thatdescribes certain seat configurations and high speed cornering. Asanother example, the computing system can generally associate passengerpreference data 406 indicating discomfort with certain seatconfigurations and highway travel. In such fashion, the computing systemcan predict an optimal seat configuration based on the route featuredata 416 that describes the route features of the planned route.

The request characteristic(s) data 400 can include trip duration data418. Trip duration data 418 can describe an estimated total duration ofthe trip, and/or can describe a duration of one or more trip segments ofthe trip.

FIG. 5 depicts an example of initial seat configuration data 500according to example implementations of the present disclosure. Initialseat configuration data 500 can be or otherwise be included in any formof data structure (e.g., a table, array, etc.). In some implementations,the initial seat configuration data 500 can be obtained directly fromthe autonomous vehicle (e.g., via a network, etc.). Alternatively, insome implementations, the initial seat configuration data 500 can beobtained from a computing system associated with the autonomous vehicle(e.g., a third-party computing system, a routing computing system,etc.). It should be noted that the seat configuration data 500, asdescribed, describes the initial seat configuration of one seat.However, in some implementations, the initial seat configuration data500 can describe the initial seat configuration of multiple or all seatsof the autonomous vehicle. Alternatively, in some implementations, ifthe computing system is an autonomous vehicle computing system (e.g., acomputing system located onboard the autonomous vehicle, etc.) theinitial seat configuration data 500 can be obtained from the sensorsand/or a computing device of the autonomous vehicle.

Initial seat configuration data 500 can include seat position data 502.Seat position data 502 can describe the seat position for a seat withinthe cabin can be a longitudinal and/or lateral position inside thecabin. As an example, if viewing the exterior doors of the vehicle, theseat can be located longitudinally along the side of the vehicle. Moreparticularly, the seat position described by seat position data 502 cancorrespond to a position on a track (e.g., a seat track located in thefloor of the autonomous vehicle, etc.) that spans the interior of theautonomous vehicle. The seat position described by seat position data502 can be reconfigured by moving the seat along the seat track insidethe autonomous vehicle. In such fashion, the seat position data 502 ofthe initial seat configuration data 500 can describe a longitudinalposition on the track located in the floor of the autonomous vehicle.

It should be noted that, in some implementations, not all seats of theautonomous vehicle are necessarily connected to the track inside thefloor of the autonomous vehicle. Instead, the seat position data 502 candescribe a folded position of a seat in the autonomous vehicle that isnot connected to a track inside the autonomous vehicle. Moreparticularly, a seat back of one or more seats (e.g., a third row ofseats, etc.) of the autonomous vehicle can be coupled to panel(s) of theautonomous vehicle interior (e.g., aft panel(s) of the autonomousvehicle, etc.). The seat bottom(s) of these one or more seats of theautonomous vehicle can be configured to fold up to a vertical positionto be parallel to the seat backs attached to the aft panel of theinterior of the autonomous vehicle. Further, the one or more seats canbe configured to unfold in the same manner. In such fashion, one or moreseat(s) can be coupled to panel(s) of the vehicle interior, and cantherefore be enabled to fold and unfold from the back of said interiorpanel(s).

The initial seat configuration data 500 for the seat of the autonomousvehicle can include seat orientation data 504 for the seat in theautonomous vehicle. The seat orientation described by seat orientationdata 504 can describe and/or include seat base orientation data 506,backrest orientation data 508, and headrest orientation data 510. Theseat backrest can be the back-support component of the seat. The seatbackrest orientation described by backrest orientation data 508 can beconfigured to move about an angle where the seat backrest attaches to aseat base of the seat. As an example, the seat backrest can fold to beparallel to the seat base of the seat. As another example, the seatbackrest can be positioned to be perpendicular to the seat base of theseat. The seat base described by seat base orientation data 506, similarto the seat back, can move about an angle of the seat to adjust asitting angle. Additionally, the seat base can move longitudinally aboutan axis.

In some implementations, the seat base described by seat baseorientation data 506 can be moved concurrently with the seat backrest toswitch a facing direction of the seat. As an example, the seat can befacing a first direction (e.g., a passenger sitting in the seat would belooking in the first direction). The seat backrest can move inconjunction with the seat base such that the seat can subsequently facea second direction opposite that of the first direction (e.g., facingaway from one another).

The seat orientation data 504 can describe and/or include headrestorientation data 510 that describes an orientation of the headrest of aseat. The orientation of the headrest can correspond to a lateralposition of the headrest of the seat. More particularly, the headrestorientation data 510 can describe to a degree of retraction of theheadrest into the backrest of the seat. As an example, the headrest canbe oriented to fully retract inside the backrest of the seat (e.g., tofacilitate configuration of the seat into a table configuration, etc.).As another example, the headrest orientation can be configured to fullyextend outside the backrest of the seat.

FIG. 6A depicts an example of seat adjustment instructions 604Aconfigured to adjust the seat configurations of an autonomous vehicle600A to increase cargo capacity of the autonomous vehicle according toexample implementations of the present disclosure. Initial seatconfiguration 602A depicts a seat configuration to maximize thepassenger carrying capacity of the autonomous vehicle 600A (e.g., amaximum number of available seats, etc.). As an example, as depicted,the seats of the third seat row 603A are configured in a deployedsitting configuration, and the seat backs of the third seat row 603A arecoupled to the aft-most panel(s) of the autonomous vehicle 600A. Itshould be noted that the third seat row 603A is not attached to thetrack 605A. Rather, the seat backs of the seats of the third seat row603A are coupled to the aft panel(s) of the autonomous vehicle 600A toenable an expansion of cabin space for other configurations. Further,first seat row 609A is in the forward-most direction of the seat rowtrack 605A to maximize an amount of space for passengers.

Seat adjustment instructions 604A can be seat adjustment instructionsconfigured to increase a cargo capacity of the autonomous vehicle 600A.The seat adjustment instructions 604A can be provided to the autonomousvehicle 600A. In response, the autonomous vehicle 600A can reconfigureits seats to the reconfigured seat configuration 606A. In configuration606A, the seat bases of the third passenger seats 603A have been foldedto fold parallel with the seat backs of row 603A, which are coupled tothe aft panel(s) of the interior of the autonomous vehicle. As anexample, the seat bases of the seats of third seat row 603A can fold tobe parallel to the seat backs of third seat row 603A. The folded seatsof third seat row 603A can be coupled to the aft panel(s) of the vehicleinterior of the autonomous vehicle 600A. By folding the seat bases ofthird row 603A, the first seat row 609A and the second seat row 607A areprovided room to move along the seat track 605A towards the rear of theautonomous vehicle 600A, therefore increasing cargo capacity in thefront of the autonomous vehicle 600A.

It should be noted that any of the seat rows (e.g., first seat row 609A,second seat row 607A, third seat row 603A, etc.) can be folded in anyfashion to enable a configuration that increases cargo capacity of theautonomous vehicle 600A. As such, the selection of the third seat row603A, and the folding mechanism of third seat row 603A, are depictedmerely as an illustrative example. As an example, the first seat row609A could instead be coupled to fold parallel to the inner surface ofthe fore panel(s) of the autonomous vehicle 600A, and the third seat row603A can be a seat row coupled to the seat track 605A.

FIG. 6B depicts an example of seat adjustment instructions 604Bconfigured to adjust one or more seat configurations of an autonomousvehicle 600B to a table configuration according to exampleimplementations of the present disclosure. Initial seat configuration602B depicts a seat configuration to optimize passenger socialinteractions in the autonomous vehicle 600A (e.g., facilitatingface-to-face conversation between passengers, etc.). As an example, theseats of the first seat row 603B are configured in a deployed sittingconfiguration, and are directly facing the second seat row 605B (e.g., apassenger sitting in the first seat row 603A is facing a passengersitting in the second seat row 605B). Third seat row 607B is in a foldedconfiguration and attached to the back of second seat row 605B tomaximize an amount of space between seat rows 603B and 605B. Further,first seat row 603B and second seat row 605B are positioned as far awayfrom each other on the seat track 608B as possible to further maximizethe space between the respective seat rows.

Seat adjustment instructions 604B can be seat adjustment instructionsconfigured to increase a cargo capacity of the autonomous vehicle 600B.The seat adjustment instructions 604B can be provided to the autonomousvehicle 600B. In response, the autonomous vehicle 600B can reconfigureits seats to the reconfigured seat configuration 606B. In configuration606B, the third passenger seat 607B has been detached from the back ofthe second seat row 605B and unfolded to serve in a passenger-capableconfiguration. As an example, the seat bases of the seats of third seatrow 607B have been unfolded to be perpendicular to the seat backs ofthird seat row 607B. The unfolded seats of third seat row 607B can thencouple themselves to the seat track 608B. Alternatively, oradditionally, the backs of the unfolded seats of the third seat row 607Bcan attach to the rear inner surface of the autonomous vehicle 600B. Theseat backs of the second seat row 605B can be folded to be parallel tothe seat bases of second seat row 605B. More particularly, they can befolded so that the seat backs are flush with the seat bases of secondseat row 605B, and that the seat backs and seat bases of second seat row605B are parallel to the floor of the autonomous vehicle 600B. In suchfashion, the rear surface of the seat backs of second seat row 605B canbe utilized as a table surface by occupants of first seat row 603B andthird seat row 607B.

It should be noted that in some implementations, the second seat row605B can move along the seat track 608B before and/or afterreconfiguration to the table surface configuration depicted in seatconfiguration 606B. As an example, the second seat row 605B can bereconfigured to the table surface configuration and then can movealongside the track 608B to provide more table surface access to aparticular seat row (e.g., 603B, 607B, etc.). For example, the secondseat row 605B may move along the track 608B to be closer to the firstseat row 603B, therefore providing more table surface access to theoccupants of first seat row 603B. For another example, the second seatrow 605B may move along the track 608B to be equidistant from both thefirst seat row 603B and the third seat row 607C, providing equal tableaccess to occupants of both respective rows.

FIG. 6C depicts an example of seat adjustment instructions 604Cconfigured to adjust the seat configurations of an autonomous vehicle600C to modify the facing direction of one or more seats according toexample implementations of the present disclosure. Initial seatconfiguration 602C depicts a seat configuration to maximize passengersocial privacy in the autonomous vehicle 600C (e.g., minimizing jostlingand eye contact between passengers, etc.). As an example, the seats ofthe first seat row 603C are configured in a deployed sittingconfiguration, and are facing directly opposite the second seat row 605C(e.g., a passenger sitting in the first seat row 603C is looking in theopposite direction of a passenger sitting in the second seat row 605C).As depicted, the middle seat 607C of first seat row 603C is positionedslightly further forward towards the front of the autonomous vehicle600C than the other two seats of first seat row 603C. In such fashion,the arms of the occupant of the second seat 607C are moved out ofalignment with the arms of other occupants of first seat row 603C,therefore minimizing accidental physical contact between occupants ofthe first seat row 603C. Additionally, seat rows 603C and 605C arepositioned closely to the middle of the autonomous vehicle 600C alongthe seat track 608C to maximize an amount of leg room for passengers ofthe seat rows (e.g., 603C, 605C).

It should be noted that the seat offset depicted for seat 607C is merelyfor depiction, and any number of seat(s) in a seat row can be offsetfrom one another to minimize accidental physical contact betweenoccupants of a seat row. As an example, the seat row 603C could have twoseats, and seat 607C could be offset from the second seat of the seatrow. Alternatively, each of the two seats in row 603C besides seat 607Ccould be offset towards the front of the autonomous vehicle 600C. Insuch fashion, any number of seats in a seat row of the autonomousvehicle 600C can be offset in various fashions to minimize accidentalphysical contact between occupants of the seat row.

Seat adjustment instructions 604C can be seat adjustment instructionsconfigured to modify the facing direction of the seat rows of theautonomous vehicle 600B. The seat adjustment instructions 604C can beprovided to the autonomous vehicle 600C. In response, the autonomousvehicle 600C can reconfigure its seats to the reconfigured seatconfiguration 606C. In configuration 606C, the seats of first seat row603C and second seat row 605C have reversed the backrest orientation ofeach seat so that the seats is facing the opposite direction they facedin seat configuration 602C. As an example, if the seat 607C faced the“front” of the autonomous vehicle 600C in the initial configuration602C, the seat 607C now faces the “rear” of the autonomous vehicle 600Cin the configuration 606C. Further, the first seat row 603C and secondseat row 605C have moved along the seat track 608C towards the lateralends of the autonomous vehicle 600C (e.g., the “front” side, the “rear”side, etc.). In such fashion, the seat adjustment instructions 604C havegenerated a configuration 600C that maximizes an amount of space betweenoccupants of the autonomous vehicle 600C while also facilitating socialcommunication.

It should be noted that in the second configuration 600C, the seat 607Ccan still be offset from the other two seats of first seat row 603C. Insuch fashion, the configuration of first seat row 603C can facilitatecommunication with occupants of seat row 605C while still minimizingaccidental physical contact between the occupants of first seat row603C.

FIG. 7 depicts an example configurable seat layout for an autonomousvehicle according to example implementations of the present disclosure.More particularly, autonomous vehicle 700 can include a “frontmost”longitudinal surface 702 and a “rearmost” longitudinal surface 704, andcan include a “leftmost” lateral surface 710 and a “rightmost” lateralsurface 712. A seat track 706C can span the autonomous vehicle 700 froma longitudinally frontmost lateral point to a longitudinally rearmostpoint, as depicted. In some implementations, the seat track can spanalmost the entire longitudinal distance of the autonomous vehicle 700.Alternatively, or additionally, in some implementations, the seat trackcan also span laterally (e.g., from a lateral surface 710 to a lateralsurface 712). In such fashion, the seat track 706 can, in someimplementations, allow for both lateral and longitudinal movement ofeach of the seat rows 708A, 708B, and 708C.

The interior of the autonomous vehicle can include a first seat row708A, a second seat row 708B, and a third seat row 708C. It should benoted that the number of seat rows depicted is merely illustrative, andthat any number, size, or type of seats, seat rows, and/or correspondingseat tracks can be utilized in the autonomous vehicle 700. As anexample, a single seat row can be included in the autonomous vehicle700. As another example, each seat row (e.g., 708A, 708B, 708C, etc.)can each include three seats capable of longitudinal and/or lateralmovement along three corresponding tracks. As yet another example, oneor more of the seat rows 708A-708C can be replaced with a differentinterior element, such as a media display terminal (e.g., an interactivetablet surface, etc.), a table, a bed, or any other autonomous vehicleinterior element.

In some implementations, the seats of one or more seat rows can beindependent from the track 706. As an example, seat row 708C can be afolding seat row that is coupled of the seat backs of seat row 708B, asdescribed in greater detail with regards to FIGS. 6A-6C. It should benoted that each seat of the seat rows 708A, 708B, and 708C can bereconfigurable in a variety of aspects. The specifics of seatreconfiguration will be discussed in greater detail with regards to FIG.8 .

FIG. 8 depicts configurations for a passenger seat of an autonomousvehicle according to example embodiments of the present disclosure. Forexample, FIG. 8 depicts deployed configurations for an example passengerseat 800 of an autonomous vehicle according to example embodiments ofthe present disclosure. The passenger seat 800 can include a base 850 towhich the seatback 830 is pivotably coupled. In this manner, theseatback 830 can rotate about pivot point(s) 852, 862 on the base 850 toswitch the passenger seat 800 between the first configuration 805 andthe second configuration 815, and intermediate configurations 810therebetween. For instance, the seatback 830 can rotate about the pivotpoint(s) 852, 862 in a clockwise direction to switch the passenger seat800 from the first configuration 805, through the intermediateconfiguration 810, to the second configuration 815. Conversely, theseatback 830 can rotate about the pivot point(s) 852, 862 in acounterclockwise direction to switch the passenger seat 800 from thesecond configuration 815, through the intermediate configuration 810, tothe first configuration 815.

In some implementations, the seat bottom 820 can be pivotably coupled tothe base 850 of the passenger seat 800 via one or more linkage arms 860(“seat linkage arm”). For instance, the seat bottom 820 can be pivotablycoupled to the base 850 via linkage arm(s) 860. The linkage arm(s) 860can be pivotably coupled to the base 850 at the pivot points 852, 862thereon. In some implementations, the linkage arm(s) 860 can be disposedwithin a portion of the base 850 having a shape corresponding to aparallelogram. It should be understood, however, that the linkage arm(s)860 can be disposed at any suitable location on the base 850.

As shown, movement of the linkage arm(s) 860 about the pivot point(s)852, 862, respectively, can cause the seat bottom 820 to move (e.g.,translate) along the second axis 895 of the passenger seat 800. Forinstance, movement of the linkage arm(s) 860 can cause the seat bottom820 to initially rotate about the first axis 890 of the passenger seat800. More specifically, movement of the linkage arm(s) 860 can initiallycause the seat bottom 820 to rotate about the first axis 890 until thetilt angle of seat bottom 820 is 0 degrees (e.g., horizontal). The seatbottom 820 can then translate along the second axis 895 until continuedmovement (e.g., rotation) of the linkage arm(s) 860 again causes theseat bottom 820 to rotate about the first axis 890. More specifically,the continued movement of the linkage arm(s) 860 can cause the seatbottom 820 to rotate such that the seat bottom 820 is no longerhorizontal (that is, the tilt angle is not 0 degrees). It should beunderstood that the seat bottom 820 can be configured to rotate aboutthe first axis 890 when the seatback 830 is, as discussed above,rotating about the pivot point(s) 852, 862 on the base 850 to switch thepassenger seat 800 between the first configuration 805, the intermediateconfiguration 810, and the second configuration 815.

The seatback 830 of the passenger seat 800 and the seat bottom 820 ofthe passenger seat 800 can rotate in opposing directions to switch thepassenger seat 800 between the first configuration 805, the intermediateconfiguration 810, and the second configuration 815. For instance, theseat bottom 820 can rotate about the first axis 890 in thecounterclockwise direction when the seatback 830 is rotating about thepivot point(s) 852, 862 in the clockwise direction to switch thepassenger seat 800 from the first configuration 805 to the secondconfiguration 815. Conversely, the seat bottom 820 can rotate about thefirst axis 890 in the clockwise direction when the seatback 830 isrotating about the pivot point(s) 852, 862 in the counterclockwisedirection to switch the passenger seat 800 from the second configuration815 to the first configuration 805.

FIG. 9 depicts a flowchart of a method 900 for generating and providingseat adjustment instructions to an autonomous vehicle according toaspects of the present disclosure. One or more portion(s) of theoperations of method 900 can be implemented by one or more computingsystems that include, for example, one or more portions of an operationscomputing system (e.g., operations computing system 102, etc.). Eachrespective portion of the method 900 can be performed by any (or anycombination) of the computing device(s) of the respective computingsystem. Moreover, one or more portion(s) of the method 900 can beimplemented as an algorithm on the hardware components of the device(s)described herein, for example, to facilitate generation of seatreconfiguration instructions. FIG. 9 depicts elements performed in aparticular order for purposes of illustration and discussion. Those ofordinary skill in the art, using the disclosures provided herein, willunderstand that the elements of any of the methods discussed herein canbe adapted, rearranged, expanded, omitted, combined, and/or modified invarious ways without deviating from the scope of the present disclosure.

At 902, the method 900 can include receiving service request data for anautonomous vehicle service that includes a service selection and requestcharacteristics. More particularly, a computing system (e.g., anautonomous vehicle computing system, an operations computing system,etc.) can obtain a service request from a user for a vehicle service.The service request can include and/or be associated with servicerequest data indicating a service provided by an autonomous vehicle. Theautonomous vehicle can be an autonomous vehicle with the capacity toreconfigure its seats based on a number of factors (e.g., the servicerequest data, etc.). The service request data can include a serviceselection. The service selection can indicate a particular service froma plurality of services offered by an autonomous vehicle and anassociated service provider (e.g., food delivery, autonomous humanpassenger transportation, pooled transportation service, deliveryservices, courier services, etc.). As an example, the service requestdata can indicate an autonomous vehicle transportation service at ahighest service tier of a number of service tiers (e.g., a moreluxurious transportation service, etc.). As another example, the servicerequest data may indicate a pooled transportation service (e.g., atransportation service pooled amongst a number of passengers submittingseparate service requests).

It should be noted that the autonomous vehicle providing the serviceselected by the service selection can be an autonomous vehicle capableof a plurality of seat configurations. More particularly, the autonomousvehicle can include one or more seats that can individually orcollectively be reconfigured (e.g., reconfiguration of a seatorientation and/or a seat position). As an example, a seat of theautonomous vehicle can change a location inside the autonomous vehicle(e.g., by sliding longitudinally along a track inside the cabin of theautonomous vehicle, etc.). As another example, a seat of the autonomousvehicle can change an orientation inside the autonomous vehicle (e.g.,fully retracting a headrest in the seat, changing an angle of the seatback of the seat, folding the seat back onto the seat base of the seatto form a table, etc.). In such fashion, the seating arrangement ofseats in the autonomous vehicle can be dynamically reconfigured to moreefficiently provide a number of different services.

The service request data, associated with a user's service request, caninclude one or more request characteristics. The requestcharacteristic(s) can describe aspect(s) of the requestor(s) and/or theselected service. In some implementations, the request characteristic(s)can describe an expected occupancy. As an example, the requestcharacteristic(s) can indicate if a requestor of the service hasindicated additional passengers for an autonomous transportation service(e.g., children, family members, friends, etc.). In someimplementations, the expected occupancy can include occupancypredictions associated with the selected service. As an example, thecomputing system may determine (e.g., using machine-learned model(s),passenger data, etc.) that a service requestor generally travels withmore than one person. As another example, the expected occupancypredictions may indicate a certain number of predicted occupants basedon the service selection (e.g., a service selection for a certain tierof an autonomous transportation service, an pooled transportationservice selection, etc.). The expected occupancy predictions can begenerated using various methods by the computing system (e.g.,machine-learning techniques, deterministic prediction algorithms, etc.).

In some implementations, the expected occupancy predictions can be basedat least in part on previously collected autonomous vehicle sensor dataassociated with the requestor. As an example, previous servicerequest(s) from the requestor can be associated with sensor data thatindicated the presence of two additional passengers in the vehicle(e.g., collected from seat weight sensors, door sensors, etc.). In suchfashion, the computing system can use previously collected sensor datato associate a usual number of additional passengers with a certainrequestor.

In some implementations, the expected occupancy predictions can includea prediction of specific identity(s) of passenger(s) predicted toaccompany the requestor. As an example, a requestor may historicallyrequest a certain tier of an autonomous vehicle transportation servicewhen traveling with a spouse. Based on the service tier selection, thecomputing system can predict that the spouse of the requestor willaccompany the spouse in the autonomous vehicle transportation service.Additionally, in some implementations, the passenger preferences for thespouse of the above example can be included in the expected occupancypredictions. For example, passenger preference data for the spouse mayindicate that the spouse generally prefers a certain seat configurationfor the autonomous vehicle transportation service.

In some implementations, the request characteristic(s) can includepassenger preference data. Passenger preference data can indicate arequestor(s) preferred seat configuration for a vehicle. It should benoted that passenger preference data can refer to passengers of theautonomous vehicle. However, some services do not require a passenger(e.g., a food delivery service, etc.) and as such, passenger orrequestor may be used interchangeably when discussing services of theautonomous vehicle. A “passenger” of the vehicle, as described, canrefer to both the requestor of the service and any additional occupantsof the vehicle. The preferred seat configuration can be based on ananalysis of previous data associated with the requestor (e.g., userscores, user satisfaction determinations, previously selected userconfigurations, etc.). As an example, the passenger preference data mayinclude user score(s) and/or user satisfaction data associated withservices previously requested by the requestor. For example, if therequestor previously requested an autonomous transportation service at acertain tier, and the requestor left a poor review for the service aftercompletion, the seat configuration of the autonomous vehicle whileproviding the service can be disincentivized for selection (by thecomputing system) for subsequent service requests by the requestor.Moreover, a computing system (e.g., its machine-learned models, etc.)can be receive user rating(s), review(s), etc. as a feedback process tolearn which seat configurations were positively received by user(s) andwhich seat configurations were negatively received by user(s). Thecomputing system can re-train/re-learn and adjust a certain seatconfiguration for a selected service in hopes of decreasing the amountof future negative feedback.

In some implementations, the passenger preference data can includeuser-entered data corresponding to the seat configuration of theautonomous vehicle. As an example, the user-entered data may specify acertain seat configuration preference for a number of the services ofthe autonomous vehicle. For example, the user-entered data may indicatethat the user prefers a seat configuration that maximizes cabin spacewhen requesting a highest service tier for an autonomous vehicletransportation service. As another example, the user-entered data mayinclude a request to opt-out of certain seat configurations. Forexample, the user-entered data may include a request that the autonomousvehicle never use a passenger-facing seat configuration (e.g., vehicleseats facing one another, etc.) when requesting a highest service tierfor an autonomous vehicle transportation service.

In some implementations, the passenger preference data can includepreferences for two or more passengers of the autonomous vehicletransportation service. As an example, two requestors may request anautonomous vehicle pooled rideshare service. The passenger preferencedata can include specific passenger preference data for each of the tworequestors. As another example, a single requestor can request anautonomous vehicle transportation service, and the service request canindicate the identity of two additional passengers. The passengerpreference data can include specific passenger preference data for eachof the two additional passengers.

In some implementations, the computing system can aggregate thepassenger preference data for a number of passengers of the autonomousvehicle. More particularly, the passenger preference data can beaggregated such that the aggregated passenger preference data indicatesa seat configuration preference that satisfies the most passengers. Asan example, four passengers can be utilizing an autonomous vehiclepooled rideshare service. Passenger preference data for three of thepassengers can indicate that the passengers prefer a seat configurationwhere passengers do not face each other. Passenger preference data forone passenger can indicate the passenger prefers a seat configurationwhere passengers do face each other. The passenger preference data forall four passengers can be aggregated such that the aggregated passengerpreference data indicates that the passengers prefer a seatconfiguration where passengers do not face each other.

In some implementations, the passenger preference data can indicateaccessibility configurations required by a passenger. As an example,passenger preference data for a passenger who uses a wheelchair canindicate that the passenger requires a seat configuration accessible towheelchair-using occupants (e.g., an open seat configuration tofacilitate wheelchair access, an extendable wheelchair ramp, etc.). Insuch fashion, the passenger preference data can be utilized to quicklyand efficiently provide accessibility services to passengers who wouldpreviously be forced to wait for significant periods of time before aspecially configured vehicle was available for them.

In some implementations, the passenger preference data can indicatepassenger historical data. The historical data can describe previouspassenger preferences and/or behaviors that indicate a preference. Asdescribed previously, the historical data can be utilized by thecomputing system to predict an expected occupancy. As an example, thehistorical data can indicate that the passenger generally travels withtwo additional occupants when requesting an autonomous vehicletransportation service. In some implementations, the historical data canindicate previous behavior that indicates preferred configurations. Asan example, the historical data may indicate that a passenger generallybrings luggage when requesting an autonomous vehicle transportationservice to an airport, and therefore prefers a seat configuration thatmaximizes luggage capacity in the autonomous vehicle when traveling toan airport. The historical data for the passenger can store and utilizeany previous preferences and/or behaviors that relate to the selectionof an autonomous vehicle seat configuration.

In some implementations, the historical data can indicate a passengerexperience level with the autonomous vehicle transportation service.More particularly, the historical data can indicate a number of timesthe requestor and any associated passengers have utilized variousservice(s) of the autonomous vehicle service provider. As an example,the historical passenger data can indicate that a requestor has neverpreviously utilized an autonomous vehicle transportation service. Inresponse, the seat configuration can, in some implementations, beadjusted (e.g., via seat adjustment instructions) to increase thecomfort of the requestor. As another example, the historical data canindicate that a passenger associated with the requestor has neverpreviously utilized an autonomous vehicle transportation service. Inresponse, the seat configuration can, in some implementations, beadjusted (e.g., via seat adjustment instructions) to increase thecomfort of the passenger.

It should be noted that, in some implementations, the passengerpreference data associated with a passenger can be analyzed as it iscollected, utilized to generate and/or modify seat configurationpreferences, and then discarded. More particularly, the passengerpreference data may only contain a passenger's predicted seatconfiguration preferences, without containing any personalizedinformation (e.g., passenger locations, passenger behavior, etc.). Assuch, the passenger preference data can be utilized to optimally predictthe best seat configuration for a passenger without containing anypersonalized data associated with the passenger.

Additionally, or alternatively, in some implementations, passengers andrequestors of services may opt-out of any passenger preference datacollection, and/or can opt-out of the collection of specific passengerpreference data. In some implementations, passengers or requestors ofthe autonomous vehicle may be required to “opt-in” to passengerpreference data collection.

In some implementations, the request characteristic(s) can include anassociated trip service route. The associated trip service route caninclude a start location and an end location for the associated tripservice route. As an example, a requestor can request an autonomousvehicle transportation service from the requestor's location to arestaurant. The start location of the associated trip service route canbe the requestor's location and the end location for the associated tripservice route can be the restaurant.

In some implementations, the associated trip service route can includeroute features for the planned route to be navigated from the startlocation to the end location. In some implementations, the planned routecan be generated by the computing system. Alternatively, in someimplementations, the planned route can be generated by an associatedcomputing system that is communicatively coupled to the computing system(e.g., a route planning computing system, operations computing system,etc.). The route features can be one or more features of the plannedroute. The route features can include a highest speed, a type ofsteering required for the route, a route environment, a route duration,or any other type of information relevant to the route traveled by theautonomous vehicle from the start location to the end location.

As an example, the route features can indicate that the routeenvironment is generally considered a scenic route (e.g., based onaggregated passenger preference data, weather data, etc.) that is easilyviewed from certain windows of the autonomous vehicle (e.g., viewing theocean from the left side of the autonomous vehicle, etc.). As anotherexample, the route features may indicate that the route requires anumber of sharp cornering maneuvers generally unsuitable for certainseat configurations. As yet another example, the route features mayindicate that the planned route requires high-speed highway travel thatis generally considered unsuitable for certain seat configurations.

In some implementations, the route features can be aggregated over timealongside passenger data to better predict the effect of route features(e.g., using machine-learning techniques, etc.). As an example, thecomputing system can generally associate passenger data indicatingpassenger sickness with certain seat configurations and high speedcornering. As another example, the computing system can generallyassociate passenger data indicating discomfort with certain seatconfigurations and highway travel. In such fashion, the computing systemcan predict an optimal seat configuration based on the route features ofthe planned route.

At 904, the method 900 can include obtaining initial seat configurationdata for seats of an autonomous vehicle assigned to the service request.More particularly, the computing system can obtain data describing aninitial seat configuration for each of a plurality of seats of anautonomous vehicle assigned to the service request. In someimplementations, the assigned autonomous vehicle can be assigned toprovide the service by the computing system. Alternatively, in someimplementations, an associated computing system can assign theautonomous vehicle to provide the service (e.g., an operations computingsystem, etc.). In some implementations, the data describing the initialseat configuration can be obtained directly from the autonomous vehicle(e.g., via a network, etc.). Alternatively, in some implementations, thedata describing the initial seat configuration can be obtained from acomputing system associated with the autonomous vehicle (e.g., athird-party computing system, a routing computing system, etc.).Alternatively, in some implementations, if the computing system is anautonomous vehicle computing system (e.g., a computing system locatedonboard the autonomous vehicle, etc.) the data describing the initialseat configuration can be obtained from the sensors and/or a computingdevice of the autonomous vehicle.

The initial seat configuration for each seat of the autonomous vehiclecan include a seat position for the seat within the cabin of theautonomous vehicle. The seat position for the seat within the cabin canbe a longitudinal and/or lateral position inside the cabin. As anexample, if viewing the exterior doors of the vehicle, the seat can belocated longitudinally along the side of the vehicle. More particularly,the seat position can correspond to a position on a track (e.g., a seattrack located in the floor of the autonomous vehicle, etc.) that spansthe interior of the autonomous vehicle. The seat position can bereconfigured by moving the seat along the seat track inside theautonomous vehicle. In such fashion, the seat position of the initialseat configuration can describe a longitudinal position on the tracklocated in the floor of the autonomous vehicle.

It should be noted that, in some implementations, not all seats of theautonomous vehicle are necessarily connected to the track inside thefloor of the autonomous vehicle. Instead, the seat position can describea folded position of a seat in the autonomous vehicle that is notconnected to a track inside the autonomous vehicle. More particularly, afirst seat of the autonomous vehicle can be configured to fold forwardto attach to the back of a second seat of the autonomous vehicle.Further, the first seat can be configured to unfold from the back of theseat to a default seat position. In such fashion, one seat can fold andunfold from the back of another seat inside the autonomous vehicle.

The initial seat configuration for each seat of the autonomous vehiclecan include a seat orientation for each of the seats in the autonomousvehicle. The seat orientation can include a backrest, a seat base,and/or a headrest orientation. The seat backrest can be the back-supportcomponent of the seat. The seat backrest orientation can be configuredto move about an angle where the seat backrest attaches to a seat baseof the seat. As an example, the seat backrest can fold to be parallel tothe seat base of the seat. As another example, the seat backrest can bepositioned to be perpendicular to the seat base of the seat. The seatbase, similar to the seat back, can move about an angle of the seat toadjust a sitting angle. Additionally, the seat base can movelongitudinally about an axis.

In some implementations, the seat base can be moved concurrently withthe seat backrest to switch a facing direction of the seat. As anexample, the seat can be facing a first direction (e.g., a passengersitting in the seat would be looking in the first direction). The seatbackrest can move in conjunction with the seat base such that the seatcan subsequently face a second direction opposite that of the firstdirection (e.g., facing away from one another).

The seat orientation can describe an orientation of the headrest of aseat. The orientation of the headrest can correspond to a lateralposition of the headrest of the seat. More particularly, the headrestorientation can refer to a degree of retraction of the headrest into thebackrest of the seat. As an example, the headrest can be oriented tofully retract inside the backrest of the seat (e.g., to facilitateconfiguration of the seat into a table configuration, etc.). As anotherexample, the headrest orientation can be configured to fully extendoutside the backrest of the seat.

In some implementations, the initial seat configuration for each seat ofthe autonomous vehicle can include a seat direction for each of theseats in the autonomous vehicle. The seat direction for a seat candescribe a direction the seat is facing relative to other aspects of theautonomous vehicle. More particularly, the seat direction can be definedas the direction that a passenger of the seat would face. As an example,a front facing seat direction can describe a seat direction in which apassenger sitting in the seat would face longitudinally towards thefront section of the autonomous vehicle. As another example, a rearfacing seat direction can describe a seat direction in which a passengersitting in the seat would face longitudinally towards the rear sectionof vehicle. As yet another example, a side facing seat direction candescribe a seat direction in which a passenger sitting in the seat wouldface laterally towards a side section of the vehicle (e.g., tofacilitate viewing of natural scenery through vehicle windows, etc.). Insuch fashion, the seat direction of the initial seat configuration candescribe any seat direction of a seat relative to aspects of theautonomous vehicle

At 906, the method 900 can include generating seat adjustmentinstructions based on the initial cabin configuration and the servicerequest data. The seat adjustment instructions can adjust the seatconfiguration of seat(s) of the autonomous vehicle. More particularly,the computing system can generate seat adjustment instructions. The seatadjustment instructions can be based on the initial cabin configurationand the service request data. The seat adjustment instructions can beconfigured to adjust an initial seat configuration of at least one seatof the plurality of seats inside the cabin of the autonomous vehicle. Insome implementations, the seat adjustment instructions can be configuredto adjust the initial seat configuration to maximize an amount of cabinspace (e.g., based on the service request, etc.). The instructions cando so by adjusting the initial seat configuration so that the seats arepositioned on the longitudinal edges of the cabin of the autonomousvehicle. For example, the instructions can be configured to adjust aseat back orientation, a headrest orientation, a seat bottomorientation, and/or a seat position of a seat of the autonomous vehicle.

In some implementations, the seat adjustment instructions can beconfigured to adjust the seat configurations such that the seats do notface one another (e.g., based on passenger preferences indicatingpassengers prefer to avoid eye contact, etc.). As an example, the seatscan be initially configured to face one another. The seat adjustmentinstructions can be configured to adjust the seat configuration so thatthe seat backrest and seat base move in such a way that the seats faceaway from one another. Additionally, the seat adjustment instructionscan move the position of the seats so that the backs of opposing seatsare adjacent to each other in the middle of the autonomous vehiclecabin. In such fashion, the seat adjustment instructions can change aninitial passenger-facing seat configuration to a more “bench-like” seatconfiguration where the backs of the seats are positioned are againsteach other.

Alternatively, in some implementations, the seat adjustment instructionscan be configured to adjust the seat configurations such that the seatsdo face one another (e.g., based on passenger preference data indicatingthat two passengers know each other, etc.). As an example, the fare data(e.g., the fare data received in the service request) can indicate thattwo passengers (e.g., picked-up or dropped-off at the same location) aresplitting a fare for an autonomous vehicle transportation service. Inresponse, the seat adjustment instructions can be configured to adjustthe seat configuration so that the seat backrest and the seat base movein such away that the seats face each other (e.g., to facilitate eyecontact, conversation, etc.). In such fashion, the seat adjustmentinstructions can change an initial same-facing “bench-like” seatconfiguration to a “passenger-facing” seat configuration to facilitatecommunication and comfort between friends and family.

In some implementations, the seat adjustment instructions can beconfigured to adjust the seat configurations such that one or more seatsin a row are staggered such that the one or more seats are positionedslightly further in a longitudinal direction than the one or more otherseats. As an example, three seats can initially be configured to bepositioned in a row, where each seat is positioned in the exact samelongitudinal position (e.g., each seat is on an individual track wherethe longitudinal position of each seat on each track is the same). Theseat adjustment instructions can be configured to adjust thelongitudinal position of the middle of the three seats so that themiddle seat is positioned further longitudinally in the direction theseats are facing than the two side seats (e.g., based on expectedoccupancy data indicating a certain number of passengers, etc.). Asanother example, two seats can initially be configured to be positionedin a row, where each seat is positioned in the exact same longitudinalposition. The seat adjustment instructions can be configured to adjustthe longitudinal position of one of the two seats so that the seat ispositioned further longitudinally in the direction the seats are facingthan the other seat. In such fashion, one or more seats can bepositioned slightly forward in a seat-facing direction than the otherseat(s) in the row, therefore reducing the chance that passengerssitting in the seats in the row will rub shoulders.

In some implementations, the seat adjustment instructions can beconfigured to adjust the seat configurations such that one or more ofthe seats are folded into a table configuration (e.g., based onpassenger preference data indicating that a passenger is likely to workand/or to eat during the service, etc.). As an example, the seats can beinitially configured to face one another. The seat adjustmentinstructions can be configured to adjust the seat configuration so thatthe seat backrest of one seat folds to a position parallel to the seatbase (e.g. both the seat base and the seat backrest parallel to thefloor of the autonomous vehicle, etc.). Additionally, the seatadjustment instructions can move the position of the folded seat so thatthe seat is directly in front of a passenger and a relatively shortdistance from the passenger. In such fashion, the seat adjustmentinstructions can change an initial passenger-facing seat configuration“table” configuration such that the passenger can use the backrest ofthe seat as a table for eating or working during the autonomoustransportation service. Such a seat configuration can be associated witha “business” or “work” type of service selection such that a passengermay utilize the table as a working surface.

In some implementations, the seat adjustment instructions be based atleast in part on a destination location. The seat adjustmentinstructions can be configured to adjust the seat configurations to acargo capacity configuration such that seats of the autonomous vehicleare folded and/or moved to maximize the cargo capacity of the autonomousvehicle (e.g., based on a food delivery service selection, an airportdestination, etc.). For example, the destination of the vehicle can bean airport location (e.g., based on the request characteristics, etc.).Based on the airport destination, the seat adjustment instructions canmove the seat position and the seat orientation of one or more seats inthe cabin to maximize a cargo capacity of the autonomous vehicle (e.g.,folding one or more seats, etc.). It should be noted that in someimplementations, the cargo capacity configuration can have an occupiedmode and a non-occupied mode. The occupied mode can configure the seatsto maximize a cargo space in the vehicle while still allowing passengersto occupy the vehicle (e.g., maintaining some seats in an unfoldedposition, etc.), while the non-occupied mode can configure the seats tomaximize the cargo space in the vehicle without regard to the occupancyof the vehicle (e.g., folding all seats in the vehicle, etc.).

As described previously, the seat adjustment instructions can be basedat least in part on the service request data. As an example, the seatadjustment instructions can be based on the service selection. Forexample, the seat configuration can be adjusted to maximize cargo spacebased on a food delivery service selection. For another example, theseat configuration can be adjusted to maximize the passenger capacity ofthe vehicle based on an autonomous vehicle rideshare pooling serviceselection. Further, the seat adjustment instructions can be based on therequest characteristic(s). As an example, the seat configuration can beadjusted to maximize passenger capacity based on an expected occupancyprediction. As another example, the seat configuration can be adjustedto maximize the comfort of one passenger based on passenger preferencedata. As yet another example, the seat configuration can be adjusted toa seat configuration that provides accessibility features based onaccessibility data (e.g., the capability to fit a wheelchair, etc.).

In some implementations, generating the seat adjustment instructions caninclude detecting, by the computing system using one or more sensors inthe cabin of the autonomous vehicle, one or more objects inside thecabin of the autonomous vehicle. More particularly, the computing systemcan utilize sensor(s) in the cabin of the autonomous vehicle (e.g.,camera(s), weight sensor(s), etc.) to detect objects inside theautonomous vehicle (e.g., forgotten briefcases, toys, smartphones,etc.). Based on the detected objects, the computing system can generateand/or modify the seat adjustment instructions. As an example, thecomputing system can determine, based on the initial seat configurationand the service request data, to fold three seats in a row to a foldedposition. The computing system can detect a briefcase left by apassenger in the middle seat. In response, the computing system cangenerate seat adjustment instructions that fold the outer two seats inthe row into a folded position while keeping the middle seat (e.g., theseat with the briefcase) in an upright position to avoid damaging thebriefcase and/or the seat.

Alternatively, in some implementations, the example described abovecould instead lead the autonomous vehicle to take a separate action. Asan example, the autonomous vehicle may forego generating any seatadjustment instructions. Instead, the autonomous vehicle may provide anotification to the passenger associated with the item (e.g., includingit should be removed, has been left, etc.). As another example, theautonomous vehicle may return to a maintenance location so that theobject can be removed. It should be noted that the autonomous vehiclecan take any sort of action in response to detecting the presence of anobject in the interior of the cabin of the autonomous vehicle.

In some implementations, the computing system can utilize sensor data todetermine the efficiency of various seat configuration(s) (e.g., usingmachine-learning techniques, etc.). More particularly, the computingsystem can analyze various performance characteristics (e.g., passengeringress and/or egress, luggage loading and/or unloading, etc.) based onthe sensor data to determine seat configuration efficiency forfacilitating passenger utilization of the autonomous vehicle. As anexample, sensor data (e.g., weight sensor, image data, motion detectordata, etc.) can be analyzed to determine that passenger ingress takesmore time than average when utilizing a certain seat configuration.Based on the amount of time, the computing system can determine and/orassign a seat configuration efficiency value to the seat configuration.The computing system can evaluate the seat efficiency value whengenerating seat adjustment instructions.

Additionally, in some implementations, the computing system canassociate seat configuration efficiency with request characteristicsdata. More particularly, a seat efficiency value can be correlated tocertain request characteristics (e.g., a number of passengers, an amountof luggage, a trip duration, a number of stops, etc.) In such fashion, afirst seat configuration could be associated with a plurality of seatefficiency values, each seat efficiency value correlated to differentcircumstances indicated by request characteristic data. As an example,the computing system can assign a first seat efficiency value to a firstseat configuration when three passengers are traveling in the vehicle.The computing system can then assign a second seat efficiency value tothe first seat configuration when one passenger is traveling in thevehicle. When evaluating seat configuration efficiency to generate seatadjustment instructions, the computing system can utilize the seatefficiency value that corresponds to the current circumstances indicatedby the request characteristic(s). In such fashion, the computing systemcan utilize such information as a feedback learning process to determinethe most efficient seat configuration for the circumstances indicated bythe request characteristics (e.g., a number of passengers, a certaindestination, a trip duration, etc.). For example, machine-learned modelsutilized to determine/recommend seat configurations can be re-trained onsuch data to refine future determinations/recommendations.

At 908, the method 900 can include providing the seat adjustmentinstructions to the autonomous vehicle. More particularly, the computingsystem can provide the seat adjustment instructions to the autonomousvehicle. In some implementations, the computing system can be separatefrom the autonomous vehicle and can provide the seat adjustmentinstructions to the vehicle (e.g., via networks, associated first and/orthird-party computing systems, etc.). Alternatively, in someimplementations, the computing system can be included in or otherwise bean autonomous vehicle computing system of the autonomous vehicle (e.g.,physically located onboard the autonomous vehicle, etc.), and cantherefore directly adjust the seats of the autonomous vehicle based onthe seat adjustment instructions.

It should be noted that, in some implementations, the autonomous vehiclecan be configured to adjust the seats of the autonomous vehicle based onthe seat adjustment instructions before passenger(s) enter the vehicle.More particularly, the autonomous vehicle can begin and complete seatreconfiguration (e.g., based on the adjustment instructions) beforeallowing occupants to enter the vehicle. In such fashion, the autonomousvehicle can prevent any accidental injury to passengers accessing theautonomous vehicle

In some implementations, the computing system can provide the seatadjustment instructions to a plurality of autonomous vehicles. Moreparticularly, based on the receipt of the request data, the computingsystem can determine that a plurality of vehicles can be reconfiguredwith the seat reconfiguration instructions. As an example, the computingsystem (e.g., an operations computing system of an autonomous vehicleservice, etc.) can determine, based on a number of collected servicerequests, that a number of autonomous vehicles should receive the sameseat adjustment instructions. For example, the computing system candetermine that an entire fleet of autonomous vehicles (e.g., a fleet ofautonomous vehicles of the autonomous vehicle service provider, etc.)should all be reconfigured in the same manner based at least in part ondata included in the service request. As another example, the computingsystem can determine that a number of autonomous vehicles located in acertain geographic area (e.g., a high-density urban area, a low-densityrural area, etc.) should each reconfigure seating configurations usingthe same seat adjustment instructions. For example, the computing systemcan determine from a number of service requests that the vast majorityof service requests in a high-density urban area prefer a seatconfiguration that maximizes a number of passengers of the autonomousvehicle (e.g., to lower an associated ride cost, etc.). In response, thecomputing system can provide seat adjustment instructions to every ormost autonomous vehicles in the high-density urban area to reconfigurethe autonomous vehicles to a seating configuration that maximizes anumber of passengers of the autonomous vehicle. In such fashion, thecomputing system can determine an optimal default configuration for anentire fleet of autonomous vehicles and/or a subset of a fleet ofautonomous vehicles, and can reconfigure a desired number of vehiclesconcurrently (e.g., based on market demand, collated service requestdata, etc.).

FIG. 10 depicts a block diagram of an example computing system 1000according to example embodiments of the present disclosure. Variousmeans of the example autonomy computing system 1000 can be configured toperform the methods and processes described herein. For example, anautonomy computing system 1000 can include service request obtainingunit(s) 1002, seat configuration obtaining unit(s) 1004, seat adjustmentinstruction generation unit(s) 1006, seat adjustment instructionproviding unit(s) 1008 and/or other means for performing the operationsand functions described herein. In some implementations, one or more ofthe units may be implemented separately. In some implementations, one ormore units may be a part of or included in one or more other units.These means can include processor(s), microprocessor(s), graphicsprocessing unit(s), logic circuit(s), dedicated circuit(s),application-specific integrated circuit(s), programmable array logic,field-programmable gate array(s), controller(s), microcontroller(s),and/or other suitable hardware. The means can also, or alternately,include software control means implemented with a processor or logiccircuitry, for example. The means can include or otherwise be able toaccess memory such as, for example, one or more non-transitorycomputer-readable storage media, such as random-access memory, read-onlymemory, electrically erasable programmable read-only memory, erasableprogrammable read-only memory, flash/other memory device(s), dataregistrar(s), database(s), and/or other suitable hardware.

The means (e.g., service request obtaining unit(s) 1002) can beprogrammed to perform one or more algorithm(s) for carrying out theoperations and functions described herein. For instance, the means canbe configured to obtain data (e.g., service request data) from servicerequestor that indicates a service selection and one or more requestcharacteristics for the service request. A service request obtainingunit 1002 is an example of means obtaining such data from an autonomousvehicle at an operations computing system as described herein.

The means (e.g., seat configuration obtaining unit(s) 1004) can beconfigured to obtain an initial seat configuration for each seat in anautonomous vehicle. For example, the means can be configured to obtaininitial seat configuration data that describes a seat position and aseat orientation for each seat in an autonomous vehicle assigned to aservice request. A seat configuration obtaining unit(s) 1004 is oneexample of a means for obtaining an initial seat configuration for oneor more seats of an autonomous vehicle as described herein.

The means (e.g., seat adjustment instruction generation unit(s) 1006)can be configured to generate seat adjustment instructions. For example,the means can be configured to generate, based on the initial seatconfiguration and the service request data, generate seat adjustmentinstructions configured to adjust the seat position and/or the seatorientation of at least one seat. A seat adjustment instructiongeneration unit(s) 1006 is one example of a means for generating seatadjustment instructions configured to reconfigure one or more seats ofan autonomous vehicle as described herein.

The means (e.g., seat adjustment instruction providing unit(s) 1008) canbe configured to provide seat adjustment instructions. For example, themeans can be configured to provide the generated seat adjustmentinstructions to an autonomous vehicle (e.g., via a network, one orassociated computing systems, etc.). A seat adjustment instructionproviding unit(s) 1008 is one example of a means for providing thegenerated seat adjustment instructions to an autonomous vehicle asdescribed herein.

FIG. 11 depicts example system components of an example system 1100according to example embodiments of the present disclosure. The examplesystem 1100 can include the computing system 1105 (e.g., a vehiclecomputing system 112, computing system 705, etc.) and the computingsystem(s) 1150 (e.g., operations computing system 104, etc.), etc. thatare communicatively coupled over one or more network(s) 1145.

The computing system 1105 can include one or more computing device(s)1110. The computing device(s) 1110 of the computing system 1105 caninclude processor(s) 1115 and a memory 1120. The one or more processors1115 can be any suitable processing device (e.g., a processor core, amicroprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 1120 can include one or morenon-transitory computer-readable storage media, such as RAM, ROM,EEPROM, EPROM, one or more memory devices, flash memory devices, etc.,and combinations thereof.

The memory 1120 can store information that can be accessed by the one ormore processors 1115. For instance, the memory 1120 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) caninclude computer-readable instructions 1125 that can be executed by theone or more processors 1115. The instructions 1125 can be softwarewritten in any suitable programming language or can be implemented inhardware. Additionally, or alternatively, the instructions 1125 can beexecuted in logically and/or virtually separate threads on processor(s)1115.

For example, the memory 1120 can store instructions 1125 that whenexecuted by the one or more processors 1115 cause the one or moreprocessors 1115 to perform operations such as any of the operations andfunctions for which the computing systems (e.g., computing system 705,vehicle computing system 112) are configured, as described herein.

The memory 1120 can store data 1130 that can be obtained, received,accessed, written, manipulated, created, and/or stored. The data 1130can include, for instance, vehicle data, sensor data, seat configurationdata, passenger preference data, historical passenger data, serviceassignment data, reconfiguration instruction data, and/or otherdata/information described herein. In some implementations, thecomputing device(s) 1110 can obtain from and/or store data in one ormore memory device(s) that are remote from the computing system 1105such as one or more memory devices of the computing system 1150.

The computing device(s) 1110 can also include a communication interface1135 used to communicate with one or more other system(s) (e.g.,computing system 1150). The communication interface 1135 can include anycircuits, components, software, etc. for communicating via one or morenetworks (e.g., 1140). In some implementations, the communicationinterface 1135 can include for example, one or more of a communicationscontroller, receiver, transceiver, transmitter, port, conductors,software and/or hardware for communicating data/information.

The computing system 1150 can include one or more computing devices1155. The one or more computing devices 1155 can include one or moreprocessors 1160 and a memory 1165. The one or more processors 1160 canbe any suitable processing device (e.g., a processor core, amicroprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 1165 can include one or morenon-transitory computer-readable storage media, such as RAM, ROM,EEPROM, EPROM, one or more memory devices, flash memory devices, etc.,and combinations thereof.

The memory 1165 can store information that can be accessed by the one ormore processors 1160. For instance, the memory 1165 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 1175 that can be obtained, received, accessed, written,manipulated, created, and/or stored. The data 1175 can include, forinstance, vehicle data, sensor data, seat configuration data, passengerpreference data, historical passenger data, service assignment data,reconfiguration instruction data, and/or other data/informationdescribed herein. In some implementations, the computing system 1150 canobtain data from one or more memory device(s) that are remote from thecomputing system 1150.

The memory 1165 can also store computer-readable instructions 1170 thatcan be executed by the one or more processors 1160. The instructions1170 can be software written in any suitable programming language or canbe implemented in hardware. Additionally, or alternatively, theinstructions 1170 can be executed in logically and/or virtually separatethreads on processor(s) 1160. For example, the memory 1165 can storeinstructions 1170 that when executed by the one or more processors 1160cause the one or more processors 1160 to perform any of the operationsand/or functions described herein, including, for example, any of theoperations and functions of the devices described herein, and/or otheroperations and functions.

The computing device(s) 1155 can also include a communication interface1180 used to communicate with one or more other system(s). Thecommunication interface 1180 can include any circuits, components,software, etc. for communicating via one or more networks (e.g., 1140).In some implementations, the communication interface 1180 can includefor example, one or more of a communications controller, receiver,transceiver, transmitter, port, conductors, software and/or hardware forcommunicating data/information.

The network(s) 1140 can be any type of network or combination ofnetworks that allows for communication between devices. In someembodiments, the network(s) 1140 can include one or more of a local areanetwork, wide area network, the Internet, secure network, cellularnetwork, mesh network, peer-to-peer communication link and/or somecombination thereof and can include any number of wired or wirelesslinks. Communication over the network(s) 1140 can be accomplished, forinstance, via a network interface using any type of protocol, protectionscheme, encoding, format, packaging, etc.

FIG. 11 illustrates one example system 1100 that can be used toimplement the present disclosure. Other computing systems can be used aswell. Computing tasks discussed herein as being performed at a cloudservices system can instead be performed remote from the cloud servicessystem (e.g., via aerial computing devices, robotic computing devices,facility computing devices, etc.), or vice versa. Such configurationscan be implemented without deviating from the scope of the presentdisclosure. The use of computer-based systems allows for a great varietyof possible configurations, combinations, and divisions of tasks andfunctionality between and among components. Computer-implementedoperations can be performed on a single component or across multiplecomponents. Computer-implemented tasks and/or operations can beperformed sequentially or in parallel. Data and instructions can bestored in a single memory device or across multiple memory devices.

While the present subject matter has been described in detail withrespect to specific example embodiments and methods thereof, it will beappreciated that those skilled in the art, upon attaining anunderstanding of the foregoing can readily produce alterations to,variations of, and equivalents to such embodiments. Accordingly, thescope of the present disclosure is by way of example rather than by wayof limitation, and the subject disclosure does not preclude inclusion ofsuch modifications, variations and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.

1.-20. (canceled)
 21. An autonomous vehicle comprising: a plurality ofseats; one or more sensors; and one or more processors configured to:access service request data indicative of an initial seat configurationof an interior of the autonomous vehicle, wherein the initialconfiguration comprises a layout for the plurality of seats; determineoccupancy data based on an output from the one or more sensors, theoccupancy data indicative of one or more passengers occupying theautonomous vehicle; and implement seat adjustment instructionsconfigured to adjust: (i) the initial seat configuration of at least oneof the plurality of seats of the autonomous vehicle and (ii) a cargocapacity of the autonomous vehicle, the seat adjustment instructionsgenerated based on the occupancy data and the service request data. 22.The autonomous vehicle of claim 21, wherein the one or more processorsare further configured to determine a state of the one or morepassengers occupying the autonomous vehicle.
 23. The autonomous vehicleof claim 22, wherein the state of the one or more passengers isindicative of a position and an orientation of respective passengersoccupying the autonomous vehicle.
 24. The autonomous vehicle of claim21, wherein the occupancy data comprises data indicative of theautonomous vehicle being in one of an occupied mode or a non-occupiedmode.
 25. The autonomous vehicle of claim 24, wherein the occupied modeis associated with one or more seat adjustments that accommodate atleast one of (i) the one or more passengers occupying the autonomousvehicle, or (ii) one or more objects occupying the autonomous vehicle.26. The autonomous vehicle of claim 25, wherein the one or moreprocessors are further configured to detect, using the one or moresensors, the one or more passengers and the one or more objectsoccupying the autonomous vehicle.
 27. The autonomous vehicle of claim25, wherein the seat adjustment instructions adjust one or more seatsunoccupied by the one or more passengers or the one or more objects. 28.The autonomous vehicle of claim 25, wherein the seat adjustmentinstructions maintain the initial seat configuration for respectiveseats occupied by the one or more passengers or the one or more objects.29. An autonomous vehicle control system comprising: one or moreprocessors; and a memory comprising one or more computer-readable media,the memory storing instructions executable by the one or more processorsto perform operations comprising: accessing service request dataindicative of an initial seat configuration of an interior of anautonomous vehicle, wherein the initial configuration comprises a layoutfor a plurality of seats; determining occupancy data based on an outputfrom one or more sensors, the occupancy data indicative of one or morepassengers occupying the autonomous vehicle; and implementing seatadjustment instructions configured to adjust: (i) the initial seatconfiguration of at least one of the plurality of seats of theautonomous vehicle and (ii) a cargo capacity of the autonomous vehicle,the seat adjustment instructions generated based on the occupancy dataand the service request data.
 30. The autonomous vehicle control systemof claim 29, the operations further comprising determining a state ofthe one or more passengers occupying the autonomous vehicle.
 31. Theautonomous vehicle control system of claim 30, wherein the state of theone or more passengers is indicative of a position and an orientation ofrespective passengers occupying the autonomous vehicle.
 32. Theautonomous vehicle control system of claim 29, wherein the occupancydata comprises data indicative of the autonomous vehicle being in one ofan occupied mode or a non-occupied mode.
 33. The autonomous vehiclecontrol system of claim 32, wherein the occupied mode is associated withone or more seat adjustments that accommodate at least one of (i) theone or more passengers occupying the autonomous vehicle, or (ii) one ormore objects occupying the autonomous vehicle.
 34. The autonomousvehicle control system of claim 33, wherein the operations comprisedetecting, using the one or more sensors, the one or more passengers andthe one or more objects occupying the autonomous vehicle.
 35. Theautonomous vehicle control system of claim 33, wherein the seatadjustment instructions adjust one or more seats unoccupied by the oneor more passengers or the one or more objects.
 36. The autonomousvehicle control system of claim 33, wherein the seat adjustmentinstructions maintain the initial seat configuration for respectiveseats occupied by the one or more passengers or the one or more objects.37. A non-transitory computer-readable media storing instructionsexecutable by one or more processors to cause the one or more processorsto perform operations, the operations comprising: accessing servicerequest data indicative of an initial seat configuration of an interiorof an autonomous vehicle, wherein the initial configuration comprises alayout for a plurality of seats; determining occupancy data based on anoutput from one or more sensors, the occupancy data indicative of one ormore passengers occupying the autonomous vehicle; and implementing seatadjustment instructions configured to adjust: (i) the initial seatconfiguration of at least one of the plurality of seats of theautonomous vehicle and (ii) a cargo capacity of the autonomous vehicle,the seat adjustment instructions generated based on the occupancy dataand the service request data.
 38. The non-transitory computer-readablemedia of claim 37, the operations further comprising determining a stateof the one or more passengers occupying the autonomous vehicle.
 39. Thenon-transitory computer-readable media of claim 38, wherein the state ofthe one or more passengers is indicative of a position and anorientation of respective passengers occupying the autonomous vehicle.40. The non-transitory computer-readable media of claim 37, wherein theoccupancy data comprises data indicative of the autonomous vehicle beingin one of an occupied mode or a non-occupied mode.